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Sustainable Marine Structures | Volume 03 | Issue 01 | January 2021
Distributed under creative commons license 4.0 DOI: http://dx.doi.org/10.36956/sms.v3i1.354
Sustainable Marine Structures
http://ojs.nassg.org/index.php/sms/index
ARTICLE
Analyzing Potential Water Harvesting from Atmosphere in Near
Coastal Area
Ida Bagus Mandhara Brasika1,2*
Putra Bagus Panji Pamungkas2
1. Department of Marine Sciences, Universitas Udayana, Jl. Raya Kampus Unud, Jimbaran, Kuta Selatan, Badung, Bali,
80361, Indonesia
2. Center of Remote Sensing and Ocean Science (CReSOS), Universitas Udayana, Jl. P.B. Sudirman, Denpasar, Bali, 80234,
Indonesia
ARTICLE INFO ABSTRACT
Article history
Received: 26 April 2021
Accepted: 18 May 2021
Published Online: 30 May 2021
Water is a basic need. However there are many near coastal regions which
have very limited access to fresh water. The water in area close to coastal
is mainly affected by ocean, indirectly by weather/climate conditions and
directly from seawater intrusion. While abundant fresh water is actually
available in the atmosphere in the form of moisture. Recent technology,
such as Atmospheric Water Generator (AWG), is a possible solution to
gain water from our atmosphere. However, comprehensive study is need-
ed to understand the potential water harvesting in our atmosphere. Here,
we examine the water availability in the atmosphere based on several
parameters like temperature and humidity. The data are collected from
observation using WS1040 Automatic Weather Station in a year of 2020
with a half-hour interval. Then, we calculate the availability of water con-
tent during each season, especially in dry conditions. We also simulate the
water harvesting to fulfil daily basic need of fresh water. The atmospheric
parameters have shown a monsoonal pattern. Water content decrease in
atmosphere during the dry season but the water deficit occurs after the dry
season. Although water harvesting able to supply daily freshwater need,
it is not recommended to be a single source as it requires massive water
storage and high-efficient AWG.
Keywords:
Water scarcity
Intrusion
Water vapor density
Atmospheric water generator
*Corresponding Author:
Ida Bagus Mandhara Brasika,
Department of Marine Sciences & Center of Remote Sensing and Ocean Science (CReSOS), Universitas Udayana, Jl. Raya Kampus
Unud, Jimbaran, Kuta Selatan, Badung, Bali, 80361, Indonesia;
Email: mandharabrasika@unud.ac.id
1. Introduction
Water scarcity or drought is a growing issue in many
regions. There is no standard for drought definition [1]
, but
in general drought is the dry condition longer than nor-
mal and causes the availability of water below the need
[2]
. There are more than 4.3 billion people or 71% of the
global population living under conditions of moderate to
severe water scarcity at least 1 month of the year [3]
. The
report from the World Economic Forum stated that water
crises as the largest global risk in terms of potential im-
pact [4]
. The water scarcity might be caused by the high
demand or limited access of water.
In the case of Bali, the water crisis is dominantly
caused by the conflict of different user groups [5]
. The
factor of growing tourism has a significant role. However,
some areas like Karangasem (East Bali) have limited ac-](https://image.slidesharecdn.com/volume3issue1january2021-231114030958-3577dca2/75/Sustainable-Marine-Structures-Volume-03-Issue-01-January-2021-5-2048.jpg)
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Sustainable Marine Structures | Volume 03 | Issue 01 | January 2021
Distributed under creative commons license 4.0
cess to water [6]
due to their geographical condition. Due
to several research, 260 of 400 rivers in Bali had run dry
and the largest lake had dropped 3.5 metres [7]
. The climate
has worsened the condition.
Some climate variability like El Nino Southern Oscilla-
tion (ENSO) has a significant role. Indonesia precipitation
is strongly correlated with ENSO variations in the Pacific
basin [8]
. There are 43 drought events in Indonesia during
1884-1998, only 6 of them are not correlated with El Nino
[9]
. Meteorological role is more common in Bali during
moderate and strong El Nino [10]
. Rainfall pattern in Bali is
categorized as the Monsoonal season which has the driest
and longest dry season in Indonesia, compared to other
types of season. The minimum mean of rainfall might
reach 100 mm/month with the driest months between July
and September [11]
. El Nino has a significant impact on
dry conditions in Bali, mainly between June-October [12]
.
El Nino Modoki (tripole mode of El Nino) has decreased
rainfall in Bali for 8-16%[13]
.
Bali is a relatively small island which most of their
population lives near the coastal area. The near coastal
region with a high population has been threatened by salt-
water intrusion. This characteristic can be found in south
Bali such as Denpasar, Kuta, Jimbaran and others. For
example in Jimbaran (our observation station located) has
several coastal close by and the closest one is less than 3
km away. The salt groundwater in this region is mainly
caused by seawater and hydrogeochemical influence [14]
.
In other similar near coastal region with a high popula-
tion like Semarang city, salt water intrusion affects 3,44
km region from coastline [15]
. However, this region might
change over time depending on several factors such as
aquifer withdrawal, hydraulic properties and the confining
unit of coastal area[16]
.
There are many solutions offered to combat this water
crisis. Some like rainwater harvesting, greywater reuse
and solar desalination [17,18,19]
. Other potential solution is
harvesting the water directly from the atmosphere as our
air contains lots of water. On land surface, satellite can
detect the amount of water in our air is about 3,42 g/cm2
,
with standard deviation 1.82 g/cm2 [20]
. However, we need
tools to collect the water from the atmosphere, it is known
as Atmospheric Water Generator (AWG). AWG has a sim-
ilar principle with Refrigerator and Air Conditioner which
is cooling through evaporation [21]
. But before developing
AWG, we have to understand our weather condition. How
much the real water content in our area. When is the op-
timum condition for water harvesting. In this research we
observe several atmospheric parameters. Then calculate
water availability. Lastly it is simulated to model the po-
tential of water harvesting to fulfil daily water needs.
2. Data and Method
2.1 Atmospheric Observation using WS1040
In this research we observe the atmospheric condi-
tion in our station at the Faculty of Marine Science and
Fisheries, Udayana University. The observation utilizes
Automatic Weather Station (AWS) as a data collector. The
AWS is WS-1040 which is produced by Shenzen TianKan
Electronic Technology Co., Ltd. WS-1040 is a series of
wireless weather station with solar power and PC link.
This can be seen in Figure 1 [22]
.
Figure 1. Installation of WS1040 Weather Station on
Udayana University
With this equipment, we can observe several atmo-
spheric parameters such as Temperature, Humidity,
Pressure, Wind Speed, Wind Direction and Rainfall in 30
minutes timestep. The WS-1040 is placed in the open-air
area with a radius of minimum 30 meters to the closest
building. This is to reduce the bias of data caused by the
effect of building or other possible disturbance such as
trees, animals and humans.
Figure 2 shows the location of the weather station. It is
located in the open area of Faculty Marine Sciences and
Fisheries, Udayana University. The closest coast is less
than 3 km away and there are many beaches in the radius
5 km. Thus, the potential of seawater intrusion is high and
atmospheric condition is mainly affected by ocean.
Figure 2. Location of Weather Station: Faculty of Marine
Sciences and Fisheries, Udayana University
DOI: http://dx.doi.org/10.36956/sms.v3i1.354](https://image.slidesharecdn.com/volume3issue1january2021-231114030958-3577dca2/75/Sustainable-Marine-Structures-Volume-03-Issue-01-January-2021-6-2048.jpg)
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Sustainable Marine Structures | Volume 03 | Issue 01 | January 2021
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The observation was started in November 2019. How-
ever, the data that will be used in this research is from 01
January 2020 to 31 December 2021. We only focus on 3
parameters, Station Pressure (mBar), Ambient Tempera-
ture (C) and Relative Humidity (%). As they are consid-
ered in optimization design of Atmospheric Water Gener-
ator [23,24,25]
.
The daily, monthly and seasonally pattern of these at-
mospheric parameters will be observed to gain a general
understanding of the condition in our station. For the sea-
son, we divide it into 2 season, dry and rainy. This is due
to the classification of Indonesia’s rainy season. Bali is
located in the area with Monsoonal season [11]
. Dry season
is in April-September, while the rainy season is Octo-
ber-March.
2.2 Water Content at Atmosphere
To measure the amount of water in the air, we calculate
it from several atmospheric parameter such as temperature
and relative humidity. From research by [26]
, the absolute
humidity is calculated as below:
ρv =
e M
R T
a w
× k
ea and es is given by:
e RH e
a s
= ×
es
= ×
0.611 exp( )
c T
b T
+
×
Where: ρv is absolute humidity or vapor pressure (g/
m3
), ea is vapor pressure (kPA), es is saturation vapor
pressure (kPA), Mw is molecular weight of water (g/mol)
equal to 18.02 g/mol, R is universal gas constant (J/ mol
K) equal to 8.31 J/mol K. T is temperature in Celcius (o
C)
while Tk is in Kelvin (o
K). RH is relative humidity (%).
b and c both are constant, b = 17.502 and c = 240.97 for
liquid.
2.3 Model of Water Surplus/ Deficit
By calculating the amount of water content in the at-
mosphere, we can address fluctuation of potential water
harvesting. However, this information is insufficient
to explain the possibility of water harvesting from the
atmosphere to fulfil the daily water need. We have to
consider the minimum need and optimum need of water
per person.
We model this condition by fitting the gap between
water harvesting and water need in daily time-series.
The surplus and deficit of water can be seen on our
model. We simulate the extra water of each day being
stored in water storage. So, the surplus/deficit water is
calculated based on cumulative sum. The surplus day is
when the cumulative sum is positive (above 0) and the
deficit is negative (below 0). In this model we assume
there is 100% efficiency and 10.000 m3
of air is pro-
cessed in a day. The sum of surplus and deficit is also
calculated to evaluate the annual condition. This can
address the strategy of water storage.
3. Result and Discussion
3.1 Atmosphere Variability
a. Daily and Monthly Atmosphere Variability
First, we want to understand the general variability of
several atmosphere parameters during our observation
time. Figure 3 shows the daily and monthly timeseries of
Temperature, Humidity and Absolute Pressure in 2020
at the Faculty of Marine Sciences and Fisheries Campus
using weather station WS1040. The daily mean illustrates
clearly the oscillation of its variability. The monthly
timeseries can show the general condition but fail to detect
the detail. For example, humidity between October-De-
cember is low in monthly average but if we see the daily
average it increases in general. This is because during this
month the humidity is more unstable which causes more
significant changes day by day.
DOI: http://dx.doi.org/10.36956/sms.v3i1.354](https://image.slidesharecdn.com/volume3issue1january2021-231114030958-3577dca2/75/Sustainable-Marine-Structures-Volume-03-Issue-01-January-2021-7-2048.jpg)
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Sustainable Marine Structures | Volume 03 | Issue 01 | January 2021
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There are slight differences in the ranges. For ex-
ample, in dry seasons humidity might occur less than
40%. Temperature, humidity and absolute pressure are
also more unstable in the dry season compared with the
rainy season. There is a typical stable time for the pa-
rameter. Temperature and Humidity are relatively sta-
ble from 09.00 to 10.00. The absolute pressure is more
stable around 12.00.
3.2 Optimum Water Generation
By calculating absolute humidity, we get the amount
of water in every cubic of air in the atmosphere. Although
not all 100% water content can be extracted into fresh wa-
ter due to the limitation of technology, we can still under-
stand its potential by analysing the variation of absolute
humidity. Here Figure 6 simulates the daily mean of water
content of every 10.000 m3
air for each month. As water is
a daily need, we need water availability on a daily basis.
Here we plot daily mean of water content, based on abso-
lute humidity calculation, in Figure 6.
Figure 6. Daily mean of absolute humidity
Here we can see the detailed daily water content. Al-
though the monthly mean is high, it does not mean that
the water is available in the very high concentration every
day. For example, between January and March the water
content is changing drastically day-by-day. The highest
reaches almost 150 litres per day, while the lowest is less
than 25 litres per day. In the dry season, the variation of
water content is lower. It is constant around 20-50 litres
per day.
To collect water from the atmosphere, AWG (Atmo-
spheric Water Generator) is the tool. There are many types
of AWG. However, the efficiency of water generation is
mainly caused by 3 factors, Temperature, Humidity and
Flow rate. For example, AWG with a thermoelectric cool-
ing method able to produce 11-25 gram/hour [27]
. The wa-
ter generation increases with humidity and air flow rate,
optimum in 90% RH and 30 m3
/h flow rate [28]
.
3.3 Daily Fresh Water Simulation
To fulfil their fundamental water need, humans require
about 50 litres of water per person per day, although in
humid climates humans can survive with 10 to 20 litres
per person per day [29]
. In Figure 7, we illustrate the gap
between daily water need (50 litres/day/person) and daily
water supply/availability. We assume there is water stor-
age which is able to store all surplus water in previous
days. So we can understand the surplus (positive) and the
deficit (negative) of water on a daily basis.
Figure 7. water surplus/deficit simulation
Between January and May, the water supply is high-
er than water demand. The excess water can be stored
in water storage. The minimum capacity of water stor-
age needed is 2.000 litres which is seen from the peak
of the graph. Then in the period May-November, the
water supply decreases below the daily fundamental
water need. This caused a drop in water storage until
the water deficit in September-November. The water
deficit is not in the dry season, but months after the dry
season.
In total, we can extract 14.528 litres of water in a year
if we process 10.000 m3
of air every day and gain 100%
efficiency. Unfortunately, it is not enough to fulfil water
needs of a person in a year, as it still deficit around 871
litres of water.
4. Conclusions
Our atmosphere observation with WS1040 automat-
ic weather station has shown the variability of several
parameters in our location. The data with intervals 30
minutes provide detailed observation. Temperature
is more stable during the dry season from April to
September which increases periodically every month.
While in the rainy season, the unstable condition might
be caused by some disturbance such as heavy rain or
strong wind. The humidity has the opposite general pat-
tern with temperature during the dry season. However
in the rainy season, both temperature and humidity are
generally high.
These two parameters are utilized to calculate the water
content in atmosphere. Between January and May is the
highest water content in the atmosphere. The daily water
content in November-May fluctuates heavily in the day
to day basis, while in other months it is consistently low.
DOI: http://dx.doi.org/10.36956/sms.v3i1.354](https://image.slidesharecdn.com/volume3issue1january2021-231114030958-3577dca2/75/Sustainable-Marine-Structures-Volume-03-Issue-01-January-2021-9-2048.jpg)
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Sustainable Marine Structures | Volume 03 | Issue 01 | January 2021
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The water harvesting potential might reach as high as 100
litres per day.
Our model shows that harvesting 100% water from
10.000 m3
air per day is not sufficient to fulfil a person’s
fundamental daily water need (50 litres/person/day) be-
tween September and November. In the other months,
we need water storage with a minimum capacity of 2.000
litres to save the water surplus. However, with this simu-
lation, humans in the near coastal region of south Bali are
able to survive by harvesting water from the atmosphere
although not all of their fundamental daily water needs are
fulfilled. The use of water harvesting methods as a single
source of water is not recommended.
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DOI: http://dx.doi.org/10.36956/sms.v3i1.354](https://image.slidesharecdn.com/volume3issue1january2021-231114030958-3577dca2/75/Sustainable-Marine-Structures-Volume-03-Issue-01-January-2021-10-2048.jpg)
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DOI: http://dx.doi.org/10.36956/sms.v3i1.354](https://image.slidesharecdn.com/volume3issue1january2021-231114030958-3577dca2/75/Sustainable-Marine-Structures-Volume-03-Issue-01-January-2021-11-2048.jpg)
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Sustainable Marine Structures
http://ojs.nassg.org/index.php/sms/index
ARTICLE
Non-linearity Analysis of Ship Roll Gyro-stabilizer Control System
Sathit P.1*
Chatchapol C.2
Phansak I.3
1. Department of Maritime Engineering, Faculty of International Maritime Studies, Kasetsart University, Chonburi, 20230,
Thailand
2. Department of Mechanical Engineering, Faculty of Engineering, Kasetsart University, Bangkok, 10900, Thailand
3. Department of Nautical Science and Maritime Logistics, Faculty of International Maritime Studies, Kasetsart University,
Chonburi, 20230, Thailand
ARTICLE INFO ABSTRACT
Article history
Received: 19 January 2021
Accepted: 24 May 2021
Published Online: 30 May 2021
A gyro-stabilizer is the interesting system that it can apply to marine vessels
for diminishes roll motion. Today it has potentially light weight with no hy-
drodynamics drag and effective at zero forward speed. The twin-gyroscope
was chosen. Almost, the modelling for designing the system use linear
model that it might not comprehensive mission requirement such as high
sea condition. The non-linearity analysis was proved by comparison the re-
sults between linear and non-linear model of gyro-stabilizer throughout fre-
quency domain also same wave input, constrains and limitations. Moreover,
they were cross checked by simulating in time domain. The comparison of
interested of linear and non-linear close loop model in frequency domain
has demonstrated the similar characteristics but gave different values at
same frequency obviously. The results were confirmed again by simulation
in irregular beam sea on time domain and they demonstrate the difference
of behavior of both systems while the gyro-stabilizers are switching on and
off. From the resulting analysis, the non-linear gyro-stabilizer model gives
more real results that correspond to more accuracy in a designing gyro-sta-
bilizer control system for various amplitudes and frequencies operating
condition especially high sea condition.
Keywords:
Active gyro-stabilizer
Twin gyro-stabilizer
Ship large roll motion
System identification
Inverse problems
Non-linear damping moment
Non-linear restoring moment
*Corresponding Author:
Sathit P.,
Department of Maritime Engineering, Faculty of International Maritime Studies, Kasetsart University, Chonburi, 20230, Thailand;
Email: sathit.pon@ku.th
1. Introduction
Of the six modes of motions of marine vessel, roll motion
is the important mode to be realized. It is the greatest rea-
son to capsize also affect to operation of crews, passengers
comfortable and cargos damage, when a vessel is excited by
wave load at high sea. In order to diminish amplitude of roll
motion, roll stabilizer system becomes an important role.
More than 100 years, many types of roll stabilizer have
been engineered by many researchers and designer. The
following example, bilge keels, sloshing, sliding weight,
gyro, u-tube, sea-ducted, variable angle fins, hydro-foil
keel fin and rotating cylinder etc.
Since 1995, Chadwick [1]
had gathered types of roll
stabilizers and it has been classified by control method to
be passive and active control. The passive control is the
control system does not require any external power source
to operate the control device but the active control system
does opposite way [2]
. Some types of stabilizer are only
passive control as bilge keels and sloshing. Some types
are only active control such as variable angle fins and ro-
tating cylinder. And some types are both as sliding weight,](https://image.slidesharecdn.com/volume3issue1january2021-231114030958-3577dca2/75/Sustainable-Marine-Structures-Volume-03-Issue-01-January-2021-12-2048.jpg)
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Sustainable Marine Structures | Volume 03 | Issue 01 | January 2021
Distributed under creative commons license 4.0 DOI: http://dx.doi.org/10.36956/sms.v3i1.316
u-tube, sea-ducted and gyro.
Another way, Haghighi and Jahed-Motlagh [3]
have men-
tioned classification of system control types that can be clas-
sified as either external or internal control systems. The ex-
ternal control system is systems generate resisting load (forces
and moments) outside a ship hull and the internal control
system is systems generate resisting load inside[4]
. The prin-
cipal advantages of internal systems are not hydrodynamic
drag and effective zero forward but it has heavyweight,
volume penalty and there are limited in stabilisation capa-
bility. The external systems have lightweight but it creates
hydrodynamic drag and ineffective at zero forward speed[5,6]
.
The following above mention and nowadays technology, a
gyro-stabilizer become to be an interesting system at present.
Because it has a combination of internal and external control
system advantages: potentially light weight with no hydrody-
namic drag and is effective at zero forward speed.
A gyro-stabilizer system is to use resisting moments,
which moments are the cross product between angular
momentum vector of flywheel and angular velocity vector
around precession axis. The resisting moment is applied
to vehicle or other to resist external excitation moments
that are keeping minimal oscillatory amplitude of rolling
vehicle. However, this paper focus on a gyro-stabilizer is
applied to small ships.
Background stories of gyro-stabilizers, it has been
applied to various inventions. The first gyro-stabilizer ap-
plication was invented by Brennan [7]
. Brennan used twin
gyro that it had counter-rotating flywheels to stabilize
unstable vehicle (two-wheel monorail car). This invention
had similar patents [8,9,10]
. More researches, gyro-stabiliz-
ers were applied to stabilize to two-wheel vehicles such as
a bicycle [11]
and motorcycles [12]
. In 1996, Brown and his
team published about the using gyro-stabilizer that pro-
vides mechanical stabilization and steering a single-wheel
robot [13]
. Another field, NASA used the advantage of
gyro-stabilizer to control attitude of large space structures
or satellite [14]
. In additional, gyro-stabilizers have been
applied to maritime field, e.g., autonomous under water
vehicle [15,16]
, torpedo [17]
and free surface vehicle etc. The
first record of a gyro-stabilizer in marine vehicles was
found accidentally, Howell torpedo, it was installed rapid
rotation of fly-wheel. There was 16 inches a steel wheel
diameter and was spun up to 16,000 rpm. The torpedo
was experimented for locking target on U.S. Navy boat[17]
.
For the first time of free surface vehicle, a gyro-stabilizer
device was passive system that it was utilized to diminish
roll motion [18,19,20]
. Active gyro-stabilizer systems were
developed from passive systems. The first system was
proposed by Elmer Sperry in 1908 [21]
.
Recently, many researchers have proposed related new
researches of gyro-stabilizer with novel control methods.
Townsend et al. [4]
published a new active gyrostabiliser
system for ride control of marine vehicle. McGookin et al.
[22]
published application of MPC and sliding mode con-
trol to IFAC benchmark models. Perez and Steinmann [23]
demonstrated analysis of ship roll gyrostabilizer control
that revisited the modelling of coupled vessel-gyrostabilizer
and also describes design trade-offs under performance lim-
itation. Haghighi and Jahed-Motlagh [3]
proposed ship roll
stabilization via sliding mode control and gyrostabilizer.
For designing ship roll gyro-stabilizers, a preliminary
design is important thing. In order to design it, designers
need to know wave loads, ship motion (ship model) and
actuator characteristics (gyro-stabilizer model). Almost,
modelling of designing uses an equation of motion in lin-
ear model that it may not comprehensive mission require-
ment, such as large roll motion from both high amplitudes
and frequencies of waves. Normally, these have to be
non-linear model under limitations that have more correc-
tion and accuracy also failure cases from system instabili-
ty. The failure cases may not occur in linear modelling but
it was found in non-linear. From these reasons, it becomes
the motivation of this study. Both linear and non-linear
model of gyro-stabilizer and ship were concerned.
Non-linear system modelling of gyro-stabilizer was
comprised of ship rolling model and single axis gimbal
gyroscope model (non-linear equation of motion). Nor-
mally, non-linearity of gyro-stabilizer model appears
in restoring term and exciting moment term. However
non-linearity of ship roll model able to appear all terms
of equation of motion (term of inertia, damping, restoring
and exciting moment).
In order to formulate non-linear ship roll model, the
system identification method is used to find non-linear
coefficients of added inertia, damping and restoring term
of ship. The system identification methods of ship roll
motion can be found in many papers such as Masri et al.
(1993) [24]
, Chassiakos and Marsi (1996) [25]
, Liang et al.
(1997) [26]
, Liang et al. (2001) [27]
, Jang et al. (2009) [28]
,
Jang et al. (2010)[29]
, Jang (2011) [30]
and Jang et al. (2011)
[31]
etc. However, in this paper uses method of Pongdung
et al. [32]
which is the novel method and able to find all of
non-linear terms of ship model.
The objective of this presentation is to analyse non-lin-
earity of ship roll twin gyro-stabilizer control system
under limitations of wave load and precession angle via
frequency domain analysis and time domain simulation.
2. Principals and Theories
Analysis of gyro-stabilizer system has three parts
that is realised. It comprised of water wave model, ship](https://image.slidesharecdn.com/volume3issue1january2021-231114030958-3577dca2/75/Sustainable-Marine-Structures-Volume-03-Issue-01-January-2021-13-2048.jpg)
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model and gyro-stabilizer model. In order to reach the
present objective, the regular and irregular deep water
wave models are selected to set simulation cases. The ship
and gyro-stabilizer models is concerned both linear and
non-linear model to observe and analyze effects of system
non-linearity from simulation results.
The general principle of gyroscopic stabilization, its
torque is produced by gyro-stabilizer that installed in a
ship opposes roll exciting moment from water wave. This
exciting moment disturbs the angular momentum of fly-
wheel such that develops precession motion. The cross
product of flywheel angular momentum and precession
rate induces moment to resist the exciting moment in op-
posite direction [33]
. Figure 1 explains working principle of
gyro-stabilizer that is installed in a marine vessel. At pres-
ent, twin-flywheels are selected, and there are spinning
and precession angle rotate in opposite direction. Its result
cancels the side effect of gyroscopic moments in the other
directions (normally in pitch and yaw of ship). Figure 2
displays the working of twin gyro-stabilizer.
Figure 1. Illustration single gyro-stabilizer installation
and its working principle
Figure 2. Demonstration working principle of twin gy-
ro-stabilizer and elimination of its side effect of gyro-sta-
bilisation moment
2.1 Full Non-linear Gyro-stabilizer Model
The prediction of gyro-stabilizer performances was
modelled via the two equations of motions. The first equa-
tion is the ship model and the second equation is gyro-sta-
bilizer model.
Consideration a ship motion, while it is excited by wa-
ter wave that is demonstrated in Figure 3. Instantaneous,
the ship is rolled by moment of inertia in counter clock-
wise and is acted by wave, which wave free surface has
β (wave slope angle) against horizontal line. The y axis
of body fix frame has φ (roll angle) against horizontal
line. Thus θ is relative angle between roll and wave slope
angle. The following Newton’s second law, the equation
of motion of ship can be written as
( ( )) ( ) ( ) 0
I I θ θ B θ θ C θ θ
44 44 44 44
+ + + =
a
¨ ¨
(1)
where 44
I is moment of inertia of ship that is a constant
value. 44 ( )
a
I θ
is non-linear added moment of inertia
function. 44 ( )
B θ is non-linear damping moment function
and 44 ( )
C θ is non-linear restoring moment function. In
order to find non-linear functions, non-parametric system
identification method is used.
Briefly, Pongdung’s method [32]
is chosen to determine
non-linear functions because it able to find all non-linear
functions in Equation 1 synchronously. The method needs
measured motion data from free roll decay experiment or
CFD (Computational fluid dynamics) to formulate inverse
problem. Actually, the responses are outputs and are calcu-
lated via equation of motion that the non-linear functions of
each term are known variable values. On the other hand, the
responses become to input (measured data) in inverse prob-
lem and the non-linear functions become to output (unknown
variables). Each moment terms are solved by inverse prob-
lem formalism and stabilized by Landweber’s regularization
method. Its solutions are chosen the optimal solution through
L-curve criterion. Finally, the zero-crossing detection tech-
nique of measured data is compared with that solution for
identifying each moment function and reconstruction them.
For more detail can see in Pongdung et al. [32]
.
Figure 3. Rolling of ship on water wave free surface
When all non-linear functions is known, substituting
DOI: http://dx.doi.org/10.36956/sms.v3i1.316](https://image.slidesharecdn.com/volume3issue1january2021-231114030958-3577dca2/75/Sustainable-Marine-Structures-Volume-03-Issue-01-January-2021-14-2048.jpg)
 [ ( )]( )
+ φ − φ − =
I I β β B β β
[ ( )]( ) 0
44 44 44
C β β
44
+ φ− φ− + φ − φ −
a
¨ ¨ ¨ ¨
(2)
Rearranging Equation 2, it yields
+ φ − φ
= + φ−
+ φ − + φ −
I I β B β
[ ( )] [ ( )]
44 44 44
B β β C β β
C β I I β β
44 44
44 44 44
+ φ− φ+ φ − φ
(
a
¨ ¨ ¨
)
[ ( )]
(
a
¨ ¨ ¨
)
(3)
Equation 3 is the full nonlinear ship motion: left-hand
side is ship moment and right-hand side is exciting mo-
ment. The Equation 3 has the same coefficient function in
the same term of inertia, damping and restoring: the roll
angle and wave slope are equal values at steady state on
time domain, but at transient are not.
Thus, define
τ I I θ β B θ β C θ β
w a
=
+ + +
[ ( )] [ ( )] [ ( )]
44 44 44 44
¨ ¨
(4)
The wave slope β is determined from Equation 5,
which it is regular linear wave (deep water wave).
η x t η kx ωt
( , sin
)
= −
0 ( ) (5)
where 0
η is wave amplitude, k is wave number, x is
distance in x direction,ω is angular frequency and t is
time. When differentiate Equation 4 with x , it becomes
to wave slope equation follow as Equation 6. Then wave
slope velocity and acceleration are Equation 7 and 8 re-
spectively.
( )
0
,
cos( )
d x t
k t
dx
η
β η ω
= = − (6)
0 sin( )
k t
β η ω ω
= −
(7)
2
0 cos( )
k t
β η ω ω
=
− −
(8)
Thus, a model for motion of the ship in roll together
with and n -spinning-wheel gyro-stabilizer can be ex-
pressed follows as block diagram in Figure 4. Then it can
be formulated in equation of motion follow as Equation 9
and 10.
I I θ B θ C θ τ τ
44 44 44 44
+ φ+ φ + φ
= −
a w g
( ) ( ) ( )
¨ ¨
(9)
I α B α C α τ τ
g g g s p
¨
+ + = −
sin (10)
where α ,α
and α
in Equation 9 are precession angle,
precession rate and precession acceleration respectively.
The following Equation 10 g
I , and g
C are moment of
inertia, damping and restoring coefficient of gyro-stabiliz-
er about precession axis.
Equation 9 represents the full non-linear ship roll dy-
namics, while Equation 10 represents the non-linear dy-
namics of gyro-stabilizer about the precession axis. The
following Equation 9 and Equation 10 associate coupled
system, the wave-induce roll moment ( w
τ ) excite the ship
rolling. When roll motion develops, the roll rate induces a
moment about the precession axis of spinning wheels ( s
τ
). And then the spinning wheels develop precession, its re-
action moment resists on the ship with opposes direction
of the wave-induce moment ( g
τ ).
τ K α
s g
= φ
cos (11)
τ nK α α
g g
= cos (12)
where g
K is spinning angular momentum
( g spin spin
K I
ω
= )
The roll stabilization moment for passive system can
be modified the precession damping and stiffness as well
as leave the gyro to free work. For an active system, it
is controlled through the precession dynamics via the
precession control moment that is PD controller and ex-
pressed follow
p p d
K K
τ α α
= + (13)
where p
K is proportional control gain and d
K is deriv-
ative control gain. The advantage of this control law is no
needing ship roll sensors.
Figure 4. Block diagram of full non-linear twin gyro-sta-
bilizer model
DOI: http://dx.doi.org/10.36956/sms.v3i1.316](https://image.slidesharecdn.com/volume3issue1january2021-231114030958-3577dca2/75/Sustainable-Marine-Structures-Volume-03-Issue-01-January-2021-15-2048.jpg)
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2.2 Linear Gyro-stabilizer Model
In the past, analysis any control systems via equation
of motions were treated to be linear differential equation.
There are reduced complexity and able to transform to
s-domain, and then change s-domain to be frequency do-
main. At steady state, the analysis control systems through
frequency domain are proper. This section uses almost
equation from Perez and Steinmann (2009) [23]
From Equation 9 and Equation 10, the models are lin-
earized: for small angle of roll and precession, the coef-
ficients of left-hand side are constant value. However, let
define,
φ ≈ φ
cosα (14)
α α α
cos ≈ (15)
and
sin α α
≈ (16)
Hence, the linear equation expressed as follow:
(I I B C τ τ
44 44 44 44
+ φ+ φ + φ
= −
a w g
)
¨
(17)
I α B α C α τ τ
g g g s p
¨
+ + = −
(18)
τ I I β B β C β
w a
= + + +
( 44 44 44 44
)
¨
(19)
τ K
s g
= φ
(20)
τ nK α
g g
= (21)
Note that, p
τ no changes in linear model. And linear
gyro-stabilizer system is demonstrated in Figure 5.
Figure 5. Block diagram linear twin gyro-stabilizer model
From zero condition, thus the open loop transfer func-
tion is
H s
ol ( )
= =
φ
τ s
ol
w (
(s
)
)
( )
I I s B s C
44 44 44 44
+ + +
a
2
1
(22)
The close loop transfer function is
(23)
where ( )
ol s
φ and ( )
cl s
φ are Laplace transforms open
and close loop roll angle respectively. And the transfer
function of precession angle to roll angle is
H s
cl ( )
= =
φ
τ s
cl
w (
(s
)
)
(I s B s C I s B s C nK s
44 44 44
2 2 ' ' 2 2
+ + + + +
I s B s C
g g g
2 ' '
)(
+ +
g g g g
)
H s
pr ( )
= = =
α s α s
φ(
(
s)
)
φ
(
(
s)
)
I s B s C
g g g
2 ' '
+ +
K s
g
(24)
where
B B K
g g d
'
= + (25)
C C K
g g p
'
= + (26)
The transfer function of precession angle to wave ex-
citing roll moment that is the result from Equation 23 and
24 is
=
H s H s H s
(
pw pr cl
I s B s C I s B s C nK s
44 44 44
( )
2 2 ' ' 2 2
= =
+ + + + +
τ s
α s
w
(
(
)
)
)(
K s
(
g g g g
g
) ( )
)
(27)
Rearranging Equation 24, it yields
H s
pr ( ) =
K
Ig
g
s2
+ +
B C
I I
g g
s
g g
' ' (28)
and the roots are
p1,2 =
− ± −
B B C
I I I
g g g
g g g
' ' '
2
4 (29)
Both roots are negative real roots (stability condition)
if and only if, 0
g
B′ , 0
g
C′ also constraint of
DOI: http://dx.doi.org/10.36956/sms.v3i1.316](https://image.slidesharecdn.com/volume3issue1january2021-231114030958-3577dca2/75/Sustainable-Marine-Structures-Volume-03-Issue-01-January-2021-16-2048.jpg)
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Figure 17. Comparison roll angle responses between
non-linear and linear gyro-stabilizer models; the linear
model of 0
η was set to be the reference
Figure 18. Comparison relative roll angle responses
between non-linear and linear gyro-stabilizer models;
the linear model of wave amplitude 0
η was set to be the
reference
Figure 19. Precession angle responses of linear and
non-linear gyro-stabilizer model of wave amplitude 0
η
and fix γ =10.6
The non-linear behaviors were analysed and explained
throughout frequency domain as they were mentioned
above. However, in reality, a
*
γ value cannot adjust base
on frequency domain as following Figure 12; a frequen-
cy cannot know immediately in time domain. Thus a
*
γ
will be selected only one value from Figure 12 so that
appropriate with operation requirements. The
*
γ value of
10.6 was selected. It locates at the right end of line LSCL
@ 0
η . The value is the highest value, which forces the
precession angle to work do not exceed the limited angle
throughout the frequency range. Then the selected value
was used for both linear and non-linear gyro-stabilizer
system. The results were plotted follow as Figure 19 - 22
in frequency domain. In order to examine the effect of
non-linearity, linear and non-linear gyro-stabilizer system
model were simulated in irregular wave model of Bret-
schneider’s [34]
method that it has significant wave height
of 0.04 m and the results were plot in Figure 23.
The following Figure 19, the precession angles of
non-linear system work under the limited angle through-
out frequency range. It has same characteristic of linear
system but has different values. Hence, the roll angle re-
sponse in Figure 20 is consequence of precession angle.
The roll angle response of non-linear system has same
characteristic of linear system but has different values as
well.
Figure 21 shows the frequency response results. The
peak of frequency response of non-linear open-loop sys-
tem that refers to the natural frequency of vessel is dif-
ferent from linear open-loop system. The curve of linear
closed-loop system is fair curve and approach to zero
throughout the frequency range. The curve of non-linear
closed-loop system has same behaviour of open-loop and
its peak is lower. According to Figure 22, the reduction
rates of linear and non-linear system are shown. The re-
duction rate refers to the efficiency of the stabilizer sys-
tem. The non-linear system has the reduction rate values
near linear system at the frequencies lower than 6 rad/s.
But at the higher frequencies value, the reduction rate val-
ues of non-linear system are lower.
According to the previous results, the
*
γ value was
selected and fixed. They are made to clearly understand
and proved that the non-linearity of non-linear model give
a lot of different results at all. Thus, the designing base
on linear model may give the wrong respond in reality. In
order to prove again, the linear and non-linear gyro-sta-
bilizer system models were simulated in same irregular
beam sea on time domain. Assume that, precession angle
is limited by mechanism at 90 degree but the
*
γ value was
selected from the limited angle at 60 degree of regular
wave amplitude in order to let it has margin to prevent a
DOI: http://dx.doi.org/10.36956/sms.v3i1.316](https://image.slidesharecdn.com/volume3issue1january2021-231114030958-3577dca2/75/Sustainable-Marine-Structures-Volume-03-Issue-01-January-2021-22-2048.jpg)
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damage when the gyro-stabilizer system get higher ampli-
tude in irregular wave.
Figure 20. Roll angle responses of open and close loop
condition of linear and non-linear gyro-stabilizer model of
wave amplitude 0
η that the close loop model was set the
fix value of γ =10.6
Figure 21. Comparison of frequency responses of open
and close loop condition of linear and non-linear gyro-sta-
bilizer model of wave amplitude 0
η that the close loop
models were set the fix value of γ =10.6
Figure 22. Comparison of reduction rate between linear
and non-linear gyro-stabilizer model of wave amplitude
0
η that the close loop models were set the fix value of γ
=10.6
In the following Figure 23, the setting of gyro-
stabilizer simulation cases (linear and non-linear
system) were simulated in irregular wave model. The
gyro-stabilizers were switched off first, and then be-
gin to switched on at 20 second of simulation time to
observe the difference of behaviors when the gyro-sta-
bilizer models were switch off and on. The systems
performances were gathered and shown in Table 2 in
root mean square. At the top of Figure 23, the exciting
moments of linear and non-linear system were shown.
They were induced from the same irregular wave mod-
el but it gave exciting moment amplitude and phase
shift slightly difference. The RMS of exciting moment
of linear model has the value less than non-linear
model so that accord to Figure 15. When the stabilizer
switched off, the vessel did not stabilize. The roll angle
response of linear model shown at the middle of Fig-
ure 23 has the RMS value more than non-linear model
slightly. These results accord to Figure 20. When the
gyro-stabilizers were switched on, the precession began
to move for stabilize the vessel, the roll angle respons-
es were reduced. The precession angle responses were
shown at the bottom of Figure 23. As the gyro-stabi-
lizer switched on, the precession angle of linear model
has RMS value more than non-linear accord to Figure
19. And the roll angle response of linear model has
RMS value lower than non-linear model. However, the
reduction rate (RR) of linear system has RMS value
more than non-linear system and so very different.
As the mentioned these results, they clearly show
that the designing via the linear model cloud makes the
gyro-stabilizer system miss the design point of mission
requirements. On the other hand, the non-linear system
model cloud gives more approach to reality for designing
follow mission requirements.
Table 2. The root mean square values (RMS) of gyro-sta-
bilizer system performances in irregular beam sea accord-
ing to Figure 23; the significant wave height and average
wave frequency is 0.04 m and 10.68 rad/s respectively.
Model w
τ [Nm] α [deg] φ [deg] RR [-]
LSOL 14.5 - 21.56 -
FNLSOL 15.38 - 18.57 -
LSCL 14.5 32.03 4.75 0.78
FNLSCL 15.05 31.13 11.46 0.38
DOI: http://dx.doi.org/10.36956/sms.v3i1.316](https://image.slidesharecdn.com/volume3issue1january2021-231114030958-3577dca2/75/Sustainable-Marine-Structures-Volume-03-Issue-01-January-2021-23-2048.jpg)
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Figure 23. Comparision of linear and non-linear gyrosta-
bilizer system model when they are swicthed off and on in
irregular wave with γ =10.
5. Conclusions
A gyro-stabilizer is the interesting system that it can
apply to marine vessels for diminishes roll motion. Today
it has potentially light weight with no hydrodynamics drag
and effective at zero forward speed. The twin-gyroscope
was chosen for this presentation because of it not has
side effect moment. Almost, the modelling for designing
the system use linear model that it might not compre-
hensive mission requirement such as high sea condition
(high wave amplitude and various frequencies). Thus, the
non-linear model becomes important role because it able
to give the interested results approach to reality more than
linear model. The non-linearity analysis was proved by
comparison the results between linear and non-linear mod-
el of gyro-stabilizer throughout frequency domain also
same wave input, constrains and limitations. Moreover,
they were cross-checked by simulating in time domain.
Actually, controller gains of the non-linear model can-
not directly determine the appropriated turning gain d
K
via frequency domain analysis. Hence the d
K of non-lin-
ear model was approximated by selecting the
*
γ value
so that resulted to d
K from linear model at the highest
frequency value of selected wave amplitude. The compar-
ison of interested results as wave exciting moment w
τ ,
precession angle α , roll angle response φ and reduction
rate of linear and non-linear close loop model in frequen-
cy domain has demonstrated the similar characteristics but
gave different values at same frequency obviously. The
results were confirmed again by simulation in irregular
beam sea on time domain and they demonstrate the differ-
ence of behavior of both systems while the gyro-stabilizer
was switching on and off.
From the resulting analysis, the non-linear gyro-sta-
bilizer model gives the results closer to realistic that cor-
respond to more accuracy in a designing gyro-stabilizer
control system for various amplitudes and frequencies
operating condition especially high sea condition.
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DOI: http://dx.doi.org/10.36956/sms.v3i1.316](https://image.slidesharecdn.com/volume3issue1january2021-231114030958-3577dca2/75/Sustainable-Marine-Structures-Volume-03-Issue-01-January-2021-24-2048.jpg)
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poses”, Transactions of the Society of Naval Archi-
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[18] Schlick, E.O. (1904a), “Device for minimising the
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[23] Perez, T. and Steinmann, P. D. “Analysis of Ship
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Sustainable Marine Structures | Volume 03 | Issue 01 | January 2021
Distributed under creative commons license 4.0 DOI: http://dx.doi.org/10.36956/sms.v3i1.378
Sustainable Marine Structures
http://ojs.nassg.org/index.php/sms/index
ARTICLE
Application of Fourth Industrial Revolution Technologies to Marine
Aquaculture for Future Food: Imperatives, Challenges and Prospects
Sitti Raehanah M. Shaleh1
Rossita Shapawi1
Abentin Estim1
Ching Fui Fui1
Ag. Asri
Ag. Ibrahim2
Audrey Daning Tuzan1
Lim Leong Seng1
Chen Cheng Ann1
Alter Jimat2
Burhan Japar3
Saleem Mustafa1*
1. Borneo Marine Research Institute, Universiti Malaysia Sabah, Kota Kinabalu, Sabah, 88400, Malaysia
2. Faculty of Computing and Informatics, Universiti Malaysia Sabah, Kota Kinabalu, Sabah, 88400, Malaysia
3. Korporasi Kemajuan Perikanan dan Nelayan (KO-NELAYAN), Wisma Pertanian Sabah, Kota Kinabalu, Sabah, 88994,
Malaysia
ARTICLE INFO ABSTRACT
Article history
Received: 25 May 2021
Accepted: 28 May 2021
Published Online: 10 June 2021
This study was undertaken to examine the options and feasibility of deploy-
ing new technologies for transforming the aquaculture sector with the ob-
jective of increasing the production efficiency. Selection of technologies to
obtain the expected outcome should, obviously, be consistent with the crite-
ria of sustainable development. There is a range of technologies being sug-
gested for driving change in aquaculture to enhance its contribution to food
security. It is necessary to highlight the complexity of issues for systems
approach that can shape the course of development of aquaculture so that it
can live-up to the expected fish demand by 2030 in addition to the current
quantity of 82.1 million tons. Some of the Fourth Industrial Revolution
(IR4.0) technologies suggested to achieve this target envisage the use of re-
al-time monitoring, integration of a constant stream of data from connected
production systems and intelligent automation in controls. This requires ap-
plication of mobile devices, internet of things (IoT), smart sensors, artificial
intelligence (AI), big data analytics, robotics as well as augmented virtual
and mixed reality. AI is receiving more attention due to many reasons. Its
use in aquaculture can happen in many ways, for example, in detecting
and mitigating stress on the captive fish which is considered critical for the
success of aquaculture. While the technology intensification in aquaculture
holds a great potential but there are constraints in deploying IR4.0 tools in
aquaculture. Possible solutions and practical options, especially with re-
spect to future food choices are highlighted in this paper.
Keywords:
Food security
Aquaculture 4.0
Digitalization
Imitation seafood
Sustainable solutions
*Corresponding Author:
Saleem Mustafa,
Borneo Marine Research Institute, Universiti Malaysia Sabah, Kota Kinabalu, Sabah, 88400, Malaysia;
Email: saleem@ums.edu.my
1. Introduction
Aquaculture has grown dramatically in the past five
decades, with total fish production from this sector
amounting to 82.1 million tons [1]
. It is widely recog-
nized as a potential sustainable solution for food secu-
rity and an answer to the need for cost-effective animal
protein with low-carbon and ecological footprint. Fish](https://image.slidesharecdn.com/volume3issue1january2021-231114030958-3577dca2/75/Sustainable-Marine-Structures-Volume-03-Issue-01-January-2021-26-2048.jpg)
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Sustainable Marine Structures | Volume 03 | Issue 01 | January 2021
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demand is projected to increase in the range of 112.1-
114.1 million tons by 2030, requiring an additional 30-
32 million tons of fish [2-4]
. This is a great challenge that
requires a real transformation. Landings from capture
fisheries are unlikely to increase due to depletion of
resources and impairment of ocean ecosystem [5]
. Aqua-
culture production should increase to ensure adequate
supplies.
Aquaculture has recently surpassed the capture
fisheries in production and is expected to share 62%
of the food fish by 2030 [6]
. The incremental research
and developments have been going on and have con-
tributed significantly to positioning of aquaculture to
where it stands today. However, it is inconceivable
to meet this high level of demand without transfor-
mation. Because the seafood demand has outweighed
production, there is a need for a fast-track approach
to producing fish using disruptive innovations and
technologies that hold promise for revolutionizing the
aquaculture industry.
Fourth Industrial Revolution (or IR4.0) offers a
range of technologies, and some can be adapted for
aquaculture systems. It is appropriate to apply the term
‘Aquaculture 4.0’ to aquatic farming driven by these
disruptive technologies. Some interesting work has ap-
peared in this area [7-11]
. When the sustainability is tak-
ing a center stage in food production, it is necessary to
generate a strategic framework comprising the purpose
or goals and drivers aligned with the United Nations’
Sustainable Development Goals (SDGs) to resonate
with the global narrative.
Aquaculture 4.0 envisages more automation in se-
lected functions, bridging the physical and digital oper-
ations through cyber-physical systems using computers
for monitoring and algorithmically controlling different
mechanisms and processes, and regulating the produc-
tion by closed-loop data. Potential areas of application
of Aquaculture 4.0 technologies are highlighted in this
paper together with the reasons for transformation, chal-
lenges and prospects with special focus on future food
choices.
2. Technology Intensification
Certain new terms such as ‘precision aquaculture’,
‘smart aquaculture’ and ‘digital farming’ are associated
with technology intensification in aquaculture. These
terms are often used interchangeably but there are sub-
tle differences. Precision aquaculture refers to use of
digital techniques in monitoring and optimization of the
culture system. This implies regulating the supply of
inputs in amounts exactly needed to prevent waste, re-
duce cost and minimize environmental impacts. Smart
farming envisages application of information and data
technologies in effective ways for better results. Digital
aquaculture combines elements of precision aquacul-
ture and smart aquaculture to advance the technology
intensification for Big Data analysis, use of web-based
platforms and machine learning among other innova-
tive tools.
Disruptive innovations and technologies of Aquacul-
ture 4.0 are believed to hold key to transforming aquacul-
ture towards seafood security targets. A comprehensive
review of the application of various technological tools,
including sensors, robots, 3D prints, drones and artificial
intelligence (AI) in aquaculture development has been
published recently [12]
. Aquaculture industries, and R D
organizations are giving increasing attention to embracing
modern technologies wherever feasible and affordable.
It is being considered as a way of making a major turn-
around in aquaculture. Many disrupting solutions are
aimed at achieving better output with reduced resource
inputs, improved nutritional quality of the harvest, and
compliance with the sustainability criteria. This will offer
a fast-track approach to meeting the fish production tar-
gets through four possible ways:
• Greater intelligent automation of the various opera-
tions.
• Bridging the physical and digital world through cy-
ber-physical systems using computers.
• Monitoring and algorithmically controlling different
mechanisms.
• Regulating production steps by closed loop data.
Inputs for deployment of modern technologies are in
the form of data collections through IoT concept such as
wireless network sensors and the analysis of big data with
the techniques of AI. For a start, some selective technolo-
gies can be tested for their potential outcomes in terms of
sustainable production.
Aquaculture 4.0 is closely associated with the afore-
mentioned technology-intensive aquatic farming. Fore
[13]
has outlined main steps in modern aquaculture sys-
tems (Figure 1) that are aimed at: 1) Improving the ac-
curacy, precision and repeatability in the farming pro-
cesses, 2) Facilitating a greater degree of automation
or inbuilt controls in regular monitoring of the farming
system, 3) Providing a dependable decision support
system characterized by a computerized program for
collecting and analyzing the data, and synthesizing
information that can be used to solve the problem by
computer algorithms or human intervention or both,
and 4) Reducing dependencies on manual labor, and
subjective assessments.](https://image.slidesharecdn.com/volume3issue1january2021-231114030958-3577dca2/75/Sustainable-Marine-Structures-Volume-03-Issue-01-January-2021-27-2048.jpg)
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OBSERVING
Sensors
Online monitoring
INTERPRETATION
Predictive models
System simulation
DECISION MAKING
Decision support
system
Artificial intelligence
ACTION
Automation
Integrated operations
Figure 1. Key steps in the production process.
The overall objective through all these interventions is
to improve productivity, resource efficiency, environmen-
tal sustainability and economic benefits. The fish stocked
in cultural facilities should be able to perform and express
its biological activities. These bio-responses need to be
observed (Step-1) and interpreted for assessment of fish
condition (Step-2) which will form the basis for making
decisions on whether or not any interventions are needed
(Step-3), and enforcing actions based on the decision for
expected outcomes (Step-4). These steps in the production
process can be assisted by appropriate technologies.
A model of digital aquaculture system comprising the
various tools of technology is presented in Figure 2.
Figure 2. Model of a smart aquaculture system. Repro-
duced with permission from Tetsuo and Kobayashi [14]
.
System integration that involves connecting the various
aquaculture sub-systems to function as a whole or com-
plete unit of operation as can be seen from Figure 2 is the
key to success of an intelligent aquaculture [15]
.
Among the Aquaculture 4.0 technologies, AI is receiv-
ing an increasing interest. In a complex system of aquatic
food production, AI deployment can happen in many
ways. It requires aligning the biological data with comput-
ers and understanding how the system works for solving
specific problems. Basically, these procedures involve Ar-
tificial Neural Network (ANN).
ANN is a computational model inspired by neural
network of the human nervous system. It mimics the path-
ways of brain function where the sense organs perceive
stimuli that are transmitted through sensory nerves to the
brain that decides how to respond to the stimuli and sends
the information through motor nerves to the effector or-
gans to act accordingly. This provides the foundation of
AI where flow of information happens through algorith-
mic pathways.
ANN comprises Nodes which in biology are termed
as Neurons (or nerve cells) [16]
. There are multiple nodes
arranged in three layers: Input Layer, Hidden Layer and
Output Layer (Figure 3).
Figure 3. Three layers of nodes in ANN.
• Input layer: Comprises the Input Nodes that bring in-
formation from external environment into the network and
pass it on to the hidden layer. Information processing and
computation are not carried out here.
• Hidden Layer: Comprises nodes that have no direct
connection with the outside environment (and hence the
name ‘hidden’ nodes). Their role is to perform computa-
tions and to transfer the information to the output nodes.
• Output Layer: Consists of nodes that are responsible
for transferring the information to the effector organs
which act according to the instructions to produce the
end-result or outcome.
Nodes from these adjacent layers have connections
between them. When an input (stimulus) enters the node,
it gets multiplied by a weight value [17]
. Weight represents
the strength of the signal (or impulse) and determines how
much influence the input will have on the output. Weights
are the real values associated with the stimulus. Because
all inputs (stimuli) are not equal, therefore, weight values
are different.
DOI: http://dx.doi.org/10.36956/sms.v3i1.378](https://image.slidesharecdn.com/volume3issue1january2021-231114030958-3577dca2/75/Sustainable-Marine-Structures-Volume-03-Issue-01-January-2021-28-2048.jpg)
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Weights are applied within the nodes of the hidden
layer. There is a system in the hidden layer called as an
‘Activation Function’ - that takes the input delivered by
input nodes and multiplies it by Weight. Hidden layer also
carries out distillation or filtration of inputs (information/
stimuli). It identifies and selects only the important in-
formation from the inputs, leaving out the information
that is redundant or of no major consequence. If the input
(impulse) is relevant and important (higher weight) it is
processed for a response. If weights are close to zero, they
have lesser importance and stimuli with such weights are
filtered out while those of higher weights are retained and
processed for response by the fish. There is one more enti-
ty called ‘Bias’ (b). Bias is added to the weighted sum and
fed through Activation Function as a constant value (=1)
which is the Bias Unit. By adding the value ‘1’ the Bias
creates a non-zero y-intercept in the linear regression. This
enables the ANN to fit the data when all inputs are equal
to zero, or in other words, to generate an output when in-
put is zero. It allows shifting linearity to either right or left
to help the model to fit the given data. Diagrammatically,
this pathway can be shown in Figure 4.
Figure 4. Response transmission pathway in ANN.
Mathematical transformation of this pathway can be
expressed by the equation:
Input (X) x Weight (W) +Bias (b) Output (Y).
Each input (X) is assigned a ‘weight’ (W) value based
on its relative importance to all the other inputs. Trans-
formation of input into valuable output forms the basis
of mathematical constructs where the hidden layer is lo-
cated between the input and output of the algorithm, and
in which weights are applied to the inputs and directed
through an Activation Function as the output. Bias is a
mathematical entity that is applied to introduce non-lin-
earity in the ANN model. Therefore, Bias is a constant
which helps the model in a way that it can fit best in a
regression and is an additional parameter in the neural
network which is used to adjust the output along with the
weighted sum of the inputs. In other words, Bias is an
assumption made by a mathematical/ computer model to
make the target function easier to process. Bias unit is not
connected to or influenced by the input layer activity. It is
a fixed value that is added but this role does not amount to
any ‘real activity’ (unlike the weight). Thus, the steepness
of the sigmoid curve depends on the weight of the inputs,
not the Bias. This is evident from the simple formula:
Output = sum (inputs x weights) + bias
From a biological perspective it is better to explain that
the Activation Function is not any structural or anatomical
entity but a functional process in the form of mathematical
equations that determine the output of a neural network [18]
.
All input values are close to Zero before weight is applied
but once multiplied by weights and summed up they get
accumulated, requiring rationalization to keep them in an
acceptable range for appropriate output. Activation Func-
tion also helps in normalizing the output of each neuron.
This envisages reorganization of weighted data and data
cleaning by eliminating duplicate and redundant data. Af-
ter normalization in the Activation Function, it is possible
to generate output that is needed by a fish to respond to
conditions it is exposed to.
As mentioned earlier, the node (neuron) receives a
series of inputs (stimuli) and these are acted upon by
Weight and Bias for transformation of input data in the
hidden layer, and then the information moves into the
next (output) layer of the ANN that tunes it to produce
the desired outcome in a specified range for the effectors
to react. Complexity of neuronal network requires such
a normalization for a rationalized output. Since there are
many factors in the cultural environment in which the fish
is exposed, the simpler form of the equation: Input (X) x
Weight (W) + Bias (B) Output should transform into
the following formula:
Output, Y = f (X1 x W1 + X2 x W2 + X3 x W3) + b.
Neurons of the hidden layer apply the ‘Activation
Function’ (f) to the weighted sum of the inputs because of
which the fish responds differently according to the nature
of stimuli and the needs of the situation. Activation Func-
tion provides the type of non-linearity between the inputs
and outputs that is biologically logical under a real-world
situation. It deserves emphasis that the linear models can-
not characterize the nature of relationship between input
and output. Without this (Activation Function), the ANN
can only generate linear models that do not represent the
practical possibilities because most real-world data is
non-linear. Response of any aquaculture animal in a cul-
tural system or even in the wild is inherently nonlinear in
nature. To elaborate it further, it characterizes a situation
DOI: http://dx.doi.org/10.36956/sms.v3i1.378](https://image.slidesharecdn.com/volume3issue1january2021-231114030958-3577dca2/75/Sustainable-Marine-Structures-Volume-03-Issue-01-January-2021-29-2048.jpg)
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where there is no straight-line or direct relationship be-
tween an independent variable and a dependent variable.
ANN models the input-output that defines the complexity
of the perception and processing of the response mecha-
nism in the fish.
In ANN, the Weight and Bias can be considered as
learnable parameters. In biological systems, this amounts
to rationalization but in ANN these parameters (Weight
and Bias) are randomized and adjusted toward the desired
values for the correct output. This is where machine learn-
ing is involved. Because the hidden layer has many neu-
rons and without activation function there will be a com-
bined linear output in response to different inputs. If that
is the case, fish response to multiple stimuli will be the
same with full energy mobilized into it! Addition of Bias
reduces the variance and hence introduces flexibility by
addition of a constant value to the sum of Input + Weight.
This is done by shifting the result of Activation Function
towards the positive or negative side. Weight reflects the
strength of the connection. It determines the amount of
influence a change in the input will exert on the output. A
low Weight value will have no change on the input, and
alternatively a higher Weight value will markedly change
the output. If Weight value is low, it means that the sen-
sation (stimulus) is within the threshold limits of the fish,
and it is not necessary for the fish to react.
3. Constraints and Options
The main factors constraining technology intensifica-
tion are: 1) Reluctance to change, especially in the case of
small enterprises, 2) Investment risk that weighs heavily
in decision-making in large-scale enterprises, 3) Short-
age of skilled human resources, and 4) Lack of gainful
employment opportunities for graduates who have the
knowledge for high-tech operations.
The prospects for aquaculture sector to transform
would be brighter with the realization of the inability of
the current systems to meet the fish production targets
and when significant benefits from technology-intensive
systems become apparent. Another reason which will
influence market perception will be when the consumers
demand to know the methods of growing food and tracer
technology gains a foothold.
There are three directions for the future growth of
aquaculture industry: Optimization of existing cultural
systems, genetic modification and new species as future
food.
3.1 Optimization of Existing Culture Systems
Aquaculture industry has a track record of improv-
ing the rearing systems. Knowledge and technological
interventions have always supported the production and
diversification of aquaculture. As a result, aquaculture
has grown at the rate of 7.5 - 9 % annually to emerge as
the fastest developing food sector [19,20]
. Yet, barring a few
selected species of fish, the success in commercial-scale
seed production of even the high-value fish remains elu-
sive. Most of the seed supplies still come from wild pop-
ulations. Captive breeding of bivalve molluscs and sea
cucumbers is not always successful. Commercial-scale
production of lobster seed in hatcheries remains a chal-
lenge. Established aquaculture systems such as those for
high-value finfish (groupers, seabass), shrimp and crab
have been increasing production to meet the demand in
the face of stabilization or decline of capture fisheries
using the existing infrastructure. These systems require
input of new knowledge for meeting the requirements of
sustainable development.
3.2 Genetic Modification
Certain genetic modifications have demonstrated a
quantum increase in fish growth. Growth rate of some
transgenic fish can be increased by 400-600% while si-
multaneously reducing the feed input [21]
. Genetically
engineered tilapia has been reported to put on weight 300
% faster [22]
. Genetically modified Atlantic salmon attains
market size in half the time (16-18 months) compared to
the conventional one that takes 32-36 months [23]
.
Potential candidate species are certainly those that fetch
a high price in the market. In Malaysia and many other
countries, the giant grouper (Epinephelus lanceolatus)
is highly valued but so far there are no attempts for gene
transfer in this species. Nevertheless, such high-value
species remain possible candidates. Even with the state-
of-the-art farming methods, good care in the hatchery and
rich diet, the giant grouper could not gain 1 kg/year. Ob-
viously, there are limits to growth. Growth can be modu-
lated but it remains within the range set by the genetic and
physiological factors. Giant grouper grows to 2.5 meters
and attains 400 kg of body weight in about 35- 40 years or
even more, gaining about 600 g/year or 1 kg/18 months [24]
.
Farmed groupers are reared for about for one year and are
generally harvested for marketing when 400 - 600 g.
Producing genetically modified organisms (GMOs) is
a controversial topic. Although market forces see benefits,
but conservation biologists are concerned about the eco-
system implications of GMOs in case of their entry into
the wild.
Consumer perception about GMOs is divided between
acceptance and rejection. Resolution of this issue will take
time. Future food security challenges and climate change
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adaptations will probably shape the cost/benefit analysis
of genetically modified fish. For now, the aquaculture can
remain focused on the non-controversial methods of pro-
duction.
3.3 New Species as Future Food
While optimization of culture systems of currently used
species will continue its transformation trajectory, new
species can be tested for potential development as future
seafood. Being new candidates, it will be easier to apply
modern technologies for starting from scratch for sustain-
able production since it will be free from the legacies of
the existing infrastructure and practices.
As a dynamic sector, aquaculture has been diversifying
and adapting over time for better production and sustain-
ability. However, now the food security challenges are
different and require more efforts for screening new can-
didate species and selecting those that possess biological
attributes needed for sustainable aquaculture. The criteria
for selecting new species should be based on multiple
qualities. Currently, out of 33,600 species of finfish re-
corded so far, only 608 species have been tried for aqua-
culture [3]
. The number of invertebrate species is much
higher. Of these, there are only 10 top farmed species, and
this number includes shellfish as well as seaweeds in addi-
tion to finfish.
3.3.1 Criteria for Selection
Adaptability to captivity at high-stocking density.
High rate of survival and fast growth.
Acceptance of artificial diets and nutritional efficien-
cy.
Ability of physiological systems to accept interven-
tions aimed at developing farmed fish as functional food.
Resilience to the effects of climate change and other
external drivers.
Amenability to farming in compliance with sustain-
ability requirements.
Consumer preference and marketability.
3.3.2 Advantages of Selecting New Species
(1) Aquaculture industry can make a fresh start, with-
out the need for replacing the existing expensive culture
infrastructure.
(2) Culture systems for the new species can be de-
signed in response to prevailing challenges.
(3) Leveraging of past experience.
(4) New systems could offer scope for:
a) Disruptive innovations.
b) Green technology based on circular / decarbonized
production models.
c) Potential for developing into functional seafood sys-
tem through bioencapsulated ration or other means.
4. Tilapia (Oreochromis niloticus) as a Model
Tilapia (Oreochromis niloticus) is the subject of an
ongoing study. It is a commercially important fish global-
ly. Tilapia ranks among the top 10 farmed species in the
world that are expected to drive future growth of aqua-
culture and is expected to yield 7.3 million tons a year by
2030 [6]
. Being one the most thoroughly investigated fin-
fish in the world, the data generated on its performance in
culture systems offers a great deal of insights needed for
AI application. Some basic attributes of tilapia specifically
suitable for AI application in areas such as stress detection
include:
A strong receptor-effector system.
Well-defined behavioral changes in response to en-
vironmental factors (stressors).
Sensory systems having a remarkable ability of per-
ceiving external stimuli and provoking reaction in a way
that helps the fish adjust to the prevailing conditions.
Stress-detecting algorithms can help in timely mitigat-
ing stress and even prevention, and in optimizing culture
conditions. Reducing stress on captive fish is vitally im-
portant for sustainable production [25]
.
The stress response of the fish has been documented
by several authors in the past [26-34]
. A simplified response
of tilapia to stress factors is shown in Table 1. A real-life
response system is complex, depending on many factors.
The response is modulated by magnitude of stress factors
and their possible interdependence, and duration of expo-
sure. This should be considered in algorithm development.
Still, such data provide a scientific basis for developing
algorithms to detect and mitigate the stress factors for op-
timizing the culture conditions.
Table 1. Response of tilapia to stress factors.
Variable Fish response
Temperature, o
C
Decrease in opercular movement with temperature
decline.
DO, mg/l
Reduced random movement, decline in swimming
activity.
Increased surfacing, crowding, sluggish response to
stimuli.
Salinity, ppt
Increased opercular rate, increased vertical move-
ment, disorientation, exhaustion, more time at
bottom.
Unionized am-
monia (mg/l)
Initial shock, increased agitation followed by decline,
increased vertical movement, disorientation.
The fish is exposed to different stimuli at the same time
DOI: http://dx.doi.org/10.36956/sms.v3i1.378](https://image.slidesharecdn.com/volume3issue1january2021-231114030958-3577dca2/75/Sustainable-Marine-Structures-Volume-03-Issue-01-January-2021-31-2048.jpg)
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7. Solutions
Quality control offers the best solution to curb malprac-
tices in the imitation seafood business. Once the consumer
interest picks up, the market is likely to expand and the
public will be better informed. The nature of ingredients,
quality assurance, price, awareness campaigns and con-
sumer appeal will determine their success in the market.
Certification procedures, traceability and blockchain will
be needed to ensure that only quality product enters the
market and consumers get what is claimed by the pro-
ducers of the food item on the label. Blockchain has the
capability to trace the entire lifecycle of food products
from origin to consumers. It can help combat fraud in the
food market, boost transparency in operations involved in
processing. It will be possible to track and trace any sea-
food in the market and might emerge as the solution to the
critically important food safety.
8. Conclusions
Aquaculture has grown rapidly over the recent decades.
With the stagnation in capture fisheries landings, aqua-
culture should further enhance its contribution to seafood
supply and food security. It has reached a stage that the
trend of incremental change can shift towards transforma-
tion using IR4.0 technologies. There is an urgent need to
examine if the current research and development efforts
are following a system / systems / systemic or systematic
approach. Investment in future food necessitates a serious
introspection for an outcome-based and solution-oriented
research that is linked to market demand and societal wel-
fare, and is also rooted in environmental sustainability.
Acknowledgement
This study was supported by Aquaculture Flagship pro-
gram of Universiti Malaysia Sabah.
References
[1] SOFIA. The state of world fisheries and aquaculture.
Food and Agriculture Organization, Rome, Italy,
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[2] Kobayashi, M., Msangi, S., Batka, M. et al. Fish to
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[3] Cai, J. and Leung, P.S. (2017). Short-term projection
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[4] FAO. The state of world fisheries and aquaculture.
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[5] Mustafa, S. and Saad, S. Coral Triangle: Marine bio-
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[6] WB. Fish farms to produce nearly two-thirds of glob-
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[8] Vik, J.O. Digi Sal: Towards the digital salmon- from
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[9] Lu, H. D., Yu, X., Liu, G. Q. Abnormal behavior
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[10] Chen, Y. Q., Li, S. F., Liu, H. M., Tao, P. Application
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[11] Helland, S. How digitalization is refining aquaculture
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[12] Mustafa,S., Estim, A., Shapawi, R., et al. Technolog-
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[13] Fore, M. Precision fish farming: A new framework to
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[14] Tetsuo, I. and Kobayashi, T. Smart aquaculture sys-
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[15] Li, D. and Li, C. Intelligent aquaculture. World
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[16] Ogajanovski, G. Everything you need to know about
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[17] Alammar, J. A visual and interactive guide to the ba-
sics of neural networks. Creative Commons Attribu-
DOI: http://dx.doi.org/10.36956/sms.v3i1.378](https://image.slidesharecdn.com/volume3issue1january2021-231114030958-3577dca2/75/Sustainable-Marine-Structures-Volume-03-Issue-01-January-2021-34-2048.jpg)
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tions, 2018.
[18] Nizrak, A. Comparison of activation function for
deep neural networks. Yildiz Technical University,
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[19] SOFIA. The state of world fisheries and aquaculture
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[20] IFFO. Aquaculture. IFFO Marine Ingredients Orga-
nization, London, UK, 2021.
[21] Towers, L. Importance of transgenic fish to global
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tor Holding Limited, Cork, Ireland, 2016.
[22] Muir, W.M. The threats and benefits of GM fish.
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[23] Williams, D. Genetically modified salmon to hit US
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[24] Tucker, J.W. Species profile: grouper aquaculture.
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[25] Aerts, J. Stress in aquaculture: a rough guide. The
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[26] Fernandes, M.N., Rantin, F.T. Relationships between
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in Nile tilapia (Oreochromis niloticus): aquacultural
consequences. Aquaculture, 1994, 27:339-346.
[27] Moreira, P.S.A. and Volpato, G.L. Conditioning of
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[28] Jian-yu, X., Xiang-wen, M., Ying, L. et al. Behav-
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[29] Xu, J., Liu, Y., Cui, S. et al. Behavioral responses of
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in dissolved oxygen levels as monitored by computer
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[30] Barreto, R.E., Volpato, G.L., Faturi, C.B., et al. Ag-
gressive behaviour traits predict physiological stress
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[31] Barreto, R.E., Miyai, C.A., Sanches, F.H.C. et al.
Blood cues induce antipredator behavior in Nile Tila-
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[32] Hassan, M., Zakariah, M.I., Wahab, W. et al. Histo-
pathological and behavioral changes in Oreochromis
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[33] King, M. and Sardella, B. The effects of acclimation
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[34] Panase, P., Saenphet, S. and Saenphet, K. Biochem-
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17-23.
DOI: http://dx.doi.org/10.36956/sms.v3i1.378](https://image.slidesharecdn.com/volume3issue1january2021-231114030958-3577dca2/75/Sustainable-Marine-Structures-Volume-03-Issue-01-January-2021-35-2048.jpg)
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capable of a maximum speed equal to or exceeding V =
3.7 displ .1667, where V is the maximum speed and displ
is the vessel displacement corresponding to the design
waterline in cubic meters. The classification of high-
speed vessels depends on the forces carrying them; Figure
1 shows these forces which support the vessels. On the
left-hand side, show the vessels supported by air, which
includes Air Cushion Vehicles (ACV), Surface-Effect
Ships (SES) and wing-in-ground craft (WIG).
Figure 1. types of high speed craft
On the right-hand side, the Vessels are supported by
a hydrostatic force which is given from the submerged
part of the vessel (buoyancy), called Displacement
vessels include conventional monohull, catamaran,
trimaran, small water-plane-area twin-hull (SWATH). In
between displacement vessels and air-supported, there is
a hydrodynamics force supported vessels as a result of
vessel forms such as planing hull or foil support including
the surface pricing foil (Hydrofoil) and Submerged Foil
(jet foil). They all suffer from the common problem of
limited payload and sensitivity to wind and sea state.
While each type of vessel has its unique characteristics
depended to form of the vessel.
1.1.1 Wing-in-ground (WIG) Craft
Wing-in-ground (WIG) crafts are supported in their
main operational mode solely by aerodynamic forces
which enable them to operate at low altitude above
the sea surface but out of direct contact with that
surface. Accordingly, their arrangement, engineering
characteristics, design, construction and operation have
a high degree of commonality with those characteristics
of aircraft. However, they operate with other waterborne
craft and must necessarily utilize the same collision
avoidance rules as conventional shipping. WIG craft is
a multimodal craft which, in its main operational mode,
flies by using ground effect above the water or some other
surface, without constant contact with such a surface
and supported in the air, mainly, by an aerodynamic lift
generated on a wing (wings), hull, or their parts, which are
intended to utilize the ground effect action [1]
. WIG crafts
are categorized according to the following types:
• Type A: a craft which is certified for operation only in
ground effect. Within prescribed operational limitations,
the structure and/or the equipment of such a craft should
exclude any technical possibility to exceed the flight
altitude over the maximum vertical extent of ground
effect.
• Type B: a craft which is certified for main operation
in ground effect and to temporarily increase its altitude
outside ground effect to a limited height, but not
exceeding 150 m above the surface, in case of emergency
and for overcoming obstacles.
• Type C: a craft which is certified for the same operation
as type B; and also for limited operation at altitude exceeding
150 m above the surface, in case of emergency and for
overcoming obstacles.
IMO and the International Civil Aviation Organization
(ICAO) have agreed that any WIG craft capable of
sustained flight outside the influence of ground effect
should also be subject to the rules and regulations of
ICAO. Other crafts, including those with limited fly-
over capability, should be covered only by the maritime
regulatory regime.
1.1.2 An Air Cushion Vehicle (ACV)
An Air Cushion Vehicle is a vessel that is completely
carried by air pressure, in the near vicinity to the surface.
It is suitable for utility over earth or water. Its flexibility
makes it a vehicle of choice in circumstances where
region remoteness, insufficient water profundity, or need
of shoreline facility. To hold the cushion of air under the
boat, it is outfitted with an elastic seal called a skirt. Air
leakage out from down the skirt compensated by lift fans.
The air pressure under the boat should be a balance for
load weight on the boat.
1.1.3 Surface Effect Ships (SES)
A Surface Effect Ship operates merely above water and
a little portion displacement, about 10%, is propped by
catamaran-similar side hulls. This sort of vessel features
a shallow draft than a routine catamaran and due to the
minimal displacement, makes significantly less wake.
Cause to the fixed side hulls, a SES has elastic seals only
at the stern and bow and demand lower lift capacity than
an ACV.
1.1.4 Jet Foils and Hydrofoils
Jet foils and hydrofoils have a more profound displac-](https://image.slidesharecdn.com/volume3issue1january2021-231114030958-3577dca2/75/Sustainable-Marine-Structures-Volume-03-Issue-01-January-2021-37-2048.jpg)
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a transition from displacement to planing speed?
1.4 Thesis Structure
The thesis is divided into six chapters:
Chapter 1 includes the dividing of high-speed vessels
depending on type of force which supported vessel, three
main forces; Air support, hydrodynamic support, and
buoyancy support. Also, it is displayed an objective and
purposes of research, and thesis organization.
Chapter 2 includes the literature reviews of planning
hull analysis techniques used to obtain hydrodynamic
performance in various waterways. Besides, the Savitsky
formula is applied to a prismatic planing hull is presented.
Chapter 3 Introduce the theory of planning hull, it
explained the main characteristics and the form types of a
planing hull. Also, it shows resistance types on a planing
hull, and effect of confined water to wave pattern and
viscous resistance.
Chapter 4 covered the steps of CFD analysis; domain
dimension selection, boundary condition siting, and grid
generation strategy which is used finite volume method
for prismatic planing hull analysis, when sailing through
the shallow channel and open water.
Chapter 5 discusses and validates numerical results
obtained from a shallow water channel include wetted
length, total resistance, hydrodynamic lift force, normal
force, and wave pattern with experimental results. Also;
compares total resistance and wave form with open water
results.
Chapter 6 includes conclusion for sailing effect in
various waterways of planning vessel and recomm-
endations for future works.
2. Literature Review
This chapter reviews the techniques which using for
planing hull analysis. Besides, the Savitsky formula is
applied to a prismatic planing hull is presented.
2.1 Background
Recently, the simulation of the hydrodynamic perfor-
mance of planing hull sailing in shallow water has become
a common practice in the yachts building community.
Hence, it is more used for the high-speed boats which
sailing near to shoreline compared with where yachts used
to navigate before. The high demand for high-speed boats
operating near shore requires good knowledge of their
behavior in three regions of speed, (subcritical, critical,
and supercritical).
There are many methods to calculate hydrodynamic cha-
racteristics of planing vessels such as analytical experimental
Figure 3. analysis approaches used for planning hull[2]
and numerical methods Figure 3 shows the most common of
these methods including the numerical and experimental
methods. The experimental methods require expensive
facilities and measurement tools. These results increased
cost and time compared with the numerical methods.
Hence there has been an increase in the use of numerical
methods for investigating the small boat resistance in
different waterways such as shallow channels and open
water. At the planing speed, the planing hull is supported
by buoyancy force and lift force which put the hull
in position, the wave-making resistance is the main
component in the total resistance. The waves system
includes the transverse wave and the divergent wave.
The divergent wave angle starts for a 19.47 degree at
subcritical speed (Kelvin wave pattern) and increases
until 90 degrees at critical speeds after that decreases in
supercritical speeds.
The first theoretical formula to consider the calculation
of maximum pressure around planing 2D sections
was proposed by Kerman [3]
, which was based on the
conservation of momentum in the analysis. His work
remained in use until equations for 3D planing surfaces by
Savander and Scorpio [4]
were introduced, which describes
potential perturbation and vortex distribution around a
planing plate.
The finite difference method was used to solve the
Kadomtsev-Petviashvili equation for a TSS model
moving near critical speed [5]
. By using the technique of
matched asymptotic expansions along with nonlinear
shallow-water wave theory, the problem is reduced to a
Kadomtsev-Petviashvili equation in the far-field, matched
with a near-field solution obtained by an improved
slender-body theory, taking the local wave elevation and
longitudinal disturbance velocity into account. The ship
can be either fixed or free to squat. Besides wave pattern
and wave resistance, the hydrodynamic lift force and
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trim moment are calculated by pressure integration in the
fixed-hull case; running sinkage and trim, by the condition
of hydrodynamic equilibrium in the free-hull case. Many
experiments were conducted in an attempt to calculate the
force and the moment on the flat bottom hull in shallow
water at fixed trim by Christopher [6]
. Furthermore, the
force and moment on a constant deadrise angle prismatic
hull by Reyling [7]
were obtained experimentally.
For series 62 hull form, residuary resistance was
computed over a range of speeds from displacement
speeds to planing speeds when the hull moving in shallow
water and it was concluded that there is an increase in
residual resistance at the subcritical speed range and a
decrease at the supercritical speed range as compared to
deep water. Besides, there was a resistance hump created
at the maximum angle of trim and the highest value of
sinkage.
The 2D+t potential flow method was used to investigate
the performance of planing hulls in calm water and was
compared with 3D Reynolds Navier Stokes Equation
(RANSE) method by Iafrati and Broglia [8]
. A comparison
between the two solutions is established to understand the
role played by three-dimensional effects, neglected within
the slender body assumption. The analysis is focused on
the evaluation of the free surface shape and of the pressure
field acting on the hull surface. It is shown that a good
agreement is achieved in terms of the free surface profile,
corrected is applied to the 2D+t solution to account for the
rise up of the water in front of the hull. The comparison
in terms of pressure distribution reveals important three-
dimensional effects in the very fore part of the hull, in the
region about the separation point, and at the transom. These
methods-based designs were presented, starting from a
non-stepped hull configuration, a multiple-step solution
was developed and an optimization of the unwetted aft
body area behind the steps was performed. The goal of
the optimization is drag reduction and dynamic stability
[9]
. The validation of the 2D+t model for single stepped
planing hull with the experimental data in terms of;
resistance, dynamic trim, and wetted surface area was
carried out by Bilandi [10]
. The obtained hydrodynamic
results have been compared against the experimental data
and it has been observed that the presented mathematical
model has reasonable accuracy, in particular, up to Froude
number 2.0. Furthermore, this mathematical model can
be a useful and fast tool for the stepped hull designers
in the early design stage to compare the different
hull configurations. It should also be noted that the
mathematical model has been developed in such a way
that it has the potential to model the sweep-back step and
transverse the vertical motions of single-stepped planing
hulls in future studies.
The RANSE method was used to predict moment and
force on a planing prismatic hull with a constant deadrise
angle equal to 20 degrees by Brizzolara and Serra [11]
.
Results obtained, in terms of drag lift forces and longitudinal
trimming moment, are compared with available
experimental (model tests made at Hydronautics towing
tank) and semi-empirical theories (Savitsky, Shuford, etc.)
commonly used by a naval architect for the prediction
of planing surface hydrodynamic performance. By the
comparison of global force components and moments and
the analysis of distributed parameters, such as pressure
on the wetted hull, tangential stresses, spray root line,
and wave elevations, some interesting conclusions can be
drawn on the accuracy of CFD codes for the prediction of
steady hydrodynamic performance of planing hulls.
Safari calculated; total resistance, added resistance,
and wave pattern numerically for model 4667-1 by using
CFD software based on finite volume method to solve the
RANS equations in different speed and depths including
deep and shallow water conditions. Also, the wave
pattern and flow field around the vessel are investigated.
For validating the method, at first, the resistance results
in deep water are compared with the experimental data
and show good agreements. Simulations are performed
in transient mode, using Volume of Fluid (VOF) and
k -ε schemes to model the free surface turbulent flow.
The results have shown that by decreasing the depth,
the shallow water-resistance of a planing vessel will be
increased [12]
. Mancini used this analysis to get; total
resistance coefficients, wetted surfaces, and dynamic
trim for the parent hull model (C1 hull) from the Naples
Systematic Series (NSS), Form hull characterized by a
warped bottom [13]
.
Moreover, heave motion, pitch angle, free surface defo-
rmation, and resistance of planing vessels were obtained
through this analysis by Wang [14]
in deep water.
The modern transverse stepped planing hull was inve-
stigated by CFD, which applies moving mesh techniques
and large eddy simulation to find the total resistance, trim,
and sinkage. These numerical results were validated with
experimental results [15]
. Moreover, Bakhtiari estimated;
the numerical results of drag, pressure distribution,
wetted surface, water spray, and wave generation by
stepped planing hull [16]
. Furthermore, the wake profile
was compared by Savitsky and Morabito empirical
formula. The morphing mesh method and k-ɛ model
were used to simulate the fluid flow around the two-
stepped hull moving freely to heave and pitch [17]
. Also,
this mesh technique was used to describe hydrodynamic
characteristics around the tunneled planing hull and it’s
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compared with experimental results [18]
.
The Smoothed Particle Hydrodynamics (SPH) method
investigated the pressure distribution on the seafloor in
very shallow water and, the change in the angle of created
divergent waves over a range of speeds [19]
.
2.2 Savitsky’s Method
This analysis for prismatic planing hull at fixed trim
and fixed beam aims to find formulas for calculating
the lift force, total resistance, wetted area, and center of
pressure. There are two steps for this analysis, a simple
case that imposes that all forces and moment affect in the
center of gravity. Next, the general case includes lift, drag,
and trust effect in a different point on the hull.
The equations are depended on huge experimental results.
The lift coefficients equation following as [20]
(2.1)
And
Also
(2.2)
Figure 4. parameters for prismatic planing hull by
Savitsky (1964)
Figure 4 interpreted the dimensions and trim angle
and deadrise angle β which are used to define the Savitsky
geometry. From Savitsky formula notices that the lift
force and drag is quale zero when the planing hull even
keel. The drag force can be found by
(2.3)
The angle of trim has an important role to make the
hull similar to a hydrofoil. It means that the hull has
a drag force and lift. Moreover, the deadrise angle βis
decreased lead to an increased lift force part. Finally, the
longitudinal center of pressure calculates by
(2.4)
Where lp the distance from the stern to the center of
pressure
The total wetted bottom area of a planning surface is
actually divided into two regions. One is aft of the spray-
root line, commonly referred to as the pressure area and
the other is forward of the spray-root line, referred to
as spray area. The pressure area is load carrying area of
the planning bottom. The forward spray area contributes
to the total drag but is not considered to support any
portion of load. An enlarged sketch of flow direction on a
deadrise surface is shown in Figure 5. It is found that the
flow in the pressure area is predominantly aft with some
transverse floe along the chines. The flow along the spray-
root line is primarily along the direction of the stagnation
line. In the spray wetted area the direction of the fluid
flow are such that the space angle between oncoming fluid
particles and stagnation line is equal to the angel between
the direction of the spray jet and the stagnation line; for
example any line of motion in the spray area is nearly
a reflection about the stagnation line of the incidence
velocity direction. Since the pressure in the spray area is
nearly atmospheric, then, by Bernoulli, the spray velocity
can be assumed to be equal to the planing speed.
Figure 5. flow direction along planning prism and extent
of spray area [20]
The total spray area, both sides, projected on a plane
along the keel line is given by
(2.5)
The Savistly formula achieved good agreement with the
experimental results but the CFD method work at a different
type of modern hull shape and various waterways.
3. Planing Hull Theory
This chapter displays the main characteristic of the
planing hull and the type's form of planing vessel. It is
defined the concept and types of resistances. Also, it
shows the effect of restricted waterways on wave-making
resistance and viscous resistance.
3.1 Planning Hull Characteristic
The planing vessels sharing with Common features ha-
ve a transom stern, bow carve, flat surfaces, hard chines,
V shape at the transom, and maximum length does not
exceed 30m. Figure 6 shows the main characteristics of
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the planing hull shape.
Figure 6. main planing hull characteristics
All the planing boat has one or more chine. It means
the points crossing the bottom and hull-side. There are
three types of chine; hard chine-like-angel, soft chine-like-
carve, and reverse chine. A hard chine is meant to throw
spray to the facets of the hull and to prevent water from
flooding the hull where it will raise resistance. Chine with
step has a considerable contribution of dynamic lift force.
The soft chine gives smoother sailing for the hull compare
with a hard chine boat. Whereas it is the maximum speed
for hard chine boats higher than the soft chine boats [21]
.
Deadrise β is the angle a hull bottom makes with the
horizontal plane viewed from ahead or astern. The right
amount of deadrises gives a boat directional stability, a
softer ride, and reduces wetted surface drag as the boat
rises on a plane. Deadrise is said to be “constant” if it
stays approximately the same from amidships to the
transom. Deadrise is “variable” if it changes from a deep
angle at amidships to a shallow angle at the transom.
Figure 7. main dimension of planning hull
The dimensions of the planning hull are considered the
part of hull characteristics shown in Figure 7.
LOA Overall length of the planing hull measure the
distance from bow to stern
LPP Length between perpendiculars, it changes when
the hull starts moving
TFP Draft forward measure the vertical distance
between the base line and waterline in a bow
TAP Draft aft measure the vertical distance between the
base line and waterline in a stern
The hull upright position, means don’t movies is
TAP = TFP
Trimming angel θ when the planning hull start go
forward the bow rising and stern immersed.
TAP ≠ TFP
3.2 Planing Hull Type
The design of the planing hull boat improves with
time to provide comfort, luxury, and increase the speed
according to customer requirements and application of
vessels. There are a lot of designs of planing to improve
the performance of the hull.
3.2.1 Prismatic Planing Hull
The Fridsma hull was produced in 1969 by the Davi-
dson Laboratory. Figure 8 shows the form of a prismatic
hull in which the deadrise angle β is fixed in all the
stations and was one beam in length. The bow had
identical planforms and elliptical keel profiles. When the
β equals zero the hull is called the flat bottom.
Figure 8. Prismatic Fridsma hull [22]
3.2.2 Warped Planing Hull
Figure 9 shows the warped hull in which the deadrise
angle changes along the length of the hull, for that demand
the change of angle along with any station. This gradual
change of angles from bow to stern transom gets better
comfortable on board and decreases fuel consumption
on a warped hull compared with a prismatic hull in the
voyage.
Figure 9. Warped hull of C945 models [23]
Through time the planing hull design developed to
decrease the total resistance and increase the speed. For
that on the warped bottom hull, a spray rail was added to
improve which improves the performance of the planing
hull by M¨uller-Graf (1991).
-
Give more lift force due to the deflection of the
spray.
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-
Decreases the spray frictional resistance to reduce
the spray wetted area on the hull.
-
Damping roll motion and more transverse stability.
- Increases deck dryness.
3.2.3 Tunneled Planing Hull
The tunnels divided the planning hull into three parts:
the main hull and two side hulls. It is called a trimaran
planning hull. Figure 10 shows the body plane of the
tunneled planning hull. The tunnel gives an additional
aerodynamic lift of the hull and less power required for
racing.
Figure 10. tunneled planing hull [18]
3.2.4 Stepped Planing Hull
The modern designs of the planing hull have one or
more transverse steps on the hull bottom. The transverse
step separates the flow of water and ventilates the aft
part of the hull. This causes a reduction of the wetted
surface area, which leads to decreased friction resistance
and improves the lift per unit area. Furthermore, it
gives damping pitch motion and improves pitch control.
Figure 11 shows the planing hull with one transverse
step. Approximately, the stepped planing gives good
performance at the high-speed range 1.5 ≤ Fn ≤ 1.75 and
more drag at the displacement speeds compare with the
same planing hull non-step.
Figure 11. The C03 model buttock line and body plan [10]
3.2.5 Air Lubricated Planing Hull
This type of boat is a lot ordinarily referred to as a
planing hull. When to move at high speed the bow lifts
out of the water and is carried by the passage of air under
that, whereas the stern is in a displacement mode and is
carried by the seawater. It is sailing similar to small boats
and private watercraft.
3.3 Planing Hull Resistance
The total resistance for bare planing hull in calm water
RT excludes the added resistance by sea wave and wind. It
is equal to the sum of the air resistance, viscous resistance,
spray resistance, and wave-making resistance.
RT=RA+RV+RS+RM(3.1)
There is an effect of restricted waterways such as finite
depth of water and baking effect. This waterway increases
total resistance compared to the unrestricted waterway.
3.3.1 Air Resistance
The air resistance is very little value in which the density
of air is much smaller than the density of water. Spatially
at low speed can be neglected. The air resistance can be
calculated by
RA=0.5ρacDAU2
(3.2)
Where ρa the density of air, CDair frictional coefficient,
A projected area on vessel above the waterline which
facing the air, U speed of a vessel.
3.3.2 Viscous Resistance
The viscous resistance RV concerns the main component
of total resistance because its value depends on tangential
force on the hull and vessel form. The viscous resistance
includes frictional resistance, RF viscous pressure resistance
RVP and Flow separation. The frictional resistance can be
calculated by
RF=0.5ρCFSU2
(3.3)
CF The frictional coefficient for smooth hull founded
by the International Towing Tank Conference (ITTC)
1957 for model ship.
(3.4)
Reynolds number is equal whereas kinematic
viscosity.
The factor (K) expresses all parameters related to the
shape of the hull like the wake, the roughness of a surface,
separation point, and eddies….etc.
RV =(1+K)*RF (3.5)
To explain the impact of fluid viscosity, boundary layer
theory can be used. This means that viscosity matters only
in a thin layer close to the surface of the hull. It is possible
to use the two-dimensional boundary layer along a flat
plate to define significant viscous flow characteristics.
A flat plate can be approximate the moistened hull
surface. If we look at the flow following the ship from
a reference frame, the ship's ward speed appears as an
incident flow on a stationary hull with velocity U, as
shown in Figure 12.
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Figure 12. sachem of the boundary layer on a flat plate [24]
3.3.3 Spray Resistance
Almost, the spray resistance starts to occur at Froude
number 0.5 and rapidly increases with speed. Its effect
of the spray resistance was divided to pressure resistance
component and frictional resistance by M¨uller-Graf
(1991).
RS=RSP(Fn)+RSF(Rn,Wn)(3.6)
Where RSP The function of Froude number, RSF the
function of the Reynolds number and Weber number
(3.7)
VSR is the spray velocity, dSR is the spray thickness,
and Ts is the surface tension at the water-air interface. A
representative value of Ts
3.3.4 Wave Making Resistance
The wave-making resistance RM is the second major
component of the total resistance. It is the resistance of a
wave, which is generated by the vessel when it moves in
calm water. Three factors affect wave-making resistance:
speed of the vessel, underwater hull form, and depth of
water. The last factor that has a strong influence on Froude
depth equal to one. The wave-making resistance cannot be
easygoing for calculating it, like viscous resistance. Kelvin
(1887) explain the waves system established when the
pressure point moving in deep water. There are two types
of waves. Figure 13 shows the divergent wave and the
transverse wave in deep water. The crest divergent wave
slopes 19.28ᵒ from the centerline. While the transverse
wave perpendicular to the centerline 90ᵒ. However, it
recently shows that this angle can be significantly smaller
at large Froude numbers [25]
.
Figure 13. The Kelvin ship wave pattern in deep water.
The included half-angle 19◦28_ of the waves is called the
Kelvin angle and is affected by the water depth [26]
3.4 Restricted Waterways Effect
The restricted waterways like the channels, lakes, ha-
rbored, and so on. Have effects on the total resistance,
because there is a restriction on the depth of water and
width or both. show the depth Froude number Fh makes
the essential role in divide the range of speed into three
regions in shallow water
(3.8)
• Fh 1 the region of subcritical speed the wave pattern
like in deep water.
• Fh ≈ 1 the region of critical speed the divergent wave
angle equals 90.
• Fh 1 the region of supercritical the divergent wave
angle equals 45 and disappear the transverse wave.
3.4.1 Effect on Wave-making Resistance
In shallow water, when increases the Froude depth Fh
the wave angle modified means that change the angle of
divergent wave and transverse in waves system shown in
Figure 14. That is a modification in the wave system as a
result of wave retardation which means the wave speed
in shallow water decrease than the deep water. All of that
leads to a change of wave-making resistance in shallow
water. Especially, at critical speed in a shallow water
channel can show the solitary waves. When Froude depth
was equal to 1, the speed of the ship was equal to the
speed of the wave, which can be calculated by
(3.9)
Figure 14. wave pattern change in shallow water
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3.4.2 Effect in Viscous Resistance
In shallow water, according to throttling the flow
between the seabed and bottom hull, The fluid flow
under the vessel increased speed and decreases pressure
supported the hull, it’s called the backflow effect, the fluid
speed rises leads to enhancement skin friction resistance,
whereas decreases water pressure under the hull, its
changes the position of the hull.
Squat phenomena definition is combined between
sinkage and trim when the hull moving in shallow water,
sinkage resulting from pressure dropping under the hull.
It causes a change of water plane area and moves center
of gravity. If the center of gravity union with from center
of buoyancy, the hull is upright. When the center of
gravity move from the center of buoyancy, a trim angle is
established between them. For all that, squat effect to:
• Increases the total resistance, viscous resistance, and
wave-making resistance when the vessel sailing forward.
• The ship sailing slow-speed and has been losing part
of maneuvering and steering.
• There will be a drop in speed in shallow water as
a result of increased resistance and reduced propulsion
efficiency.
• There is a greater tendency towards vibration as a
result of propeller-induced vibration.
A range of planing speed the hydrodynamic lift force
work opposite direction to sinkage force. So the sinkage
force reduces the buoyancy force which shares in put the
planing hull in position.
4. Methodology of Analysis
In this chapter, the methodology used in the CFD ana-
lysis is described. The chart in Figure 15 shows the CFD
results validation with experimental data. The work is
started by defining the numerical domain dimensions.
And then mesh elements are generated and boundary con-
ditions for the numerical domain are set to carry out the
analysis and obtain the results. Post-processing results are
then assessed by comparing them to experimental data. If
the results are in agreement with the experimental data the
analysis is finished. Otherwise, modifications on mesh and
setup are applied in the new analysis boundary condition
and regenerating the mesh to make the new analysis until
getting valid results.
4.1 Finite Volume Method
In this research work, the finite volume RANS code
ANSYS CFX was used to study the flow around a small
planing hull craft in a shallow water channel to predict
the hydrodynamic forces and wave patterns of the hull at
subcritical, critical, and supercritical speeds.
Figure 15. flow CFD analysis simulation
The motion of a viscous fluid is governed by the Navier-
Stokes equations, which are valid both for turbulent and
laminar flow. For an incompressible, Newtonian fluid
in three dimensions under the influence of an external
gravitational field, [27]
the Navier-Stokes read.
(4.1)
Here, the equations are formulated using tensor notation.
The indices i and j in the Navier-Stokes equations run over
the spatial coordinates x, y, and z. In these equations, Ui is
the velocity in direction i, xi is the spatial coordinate in
dimension i, t is time, ρ is the density, P is the pressure,
ν is the kinematic viscosity and gi is the gravitational
acceleration.
The finite volume method (FVM), is a numerical
method of discretizing a continuous partial differential
equation (PDE), into a set of algebraic equations. The first
step of discretization is to divide the computational domain
into a finite number of volumes, forming what is called a
mesh or a grid. Next, the PDE is integrated into each volume
by using the divergence theorem, yielding an algebraic
equation for each cell. In the centers of the cells, cell-
averaged values of the flow variables are stored in so-called
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nodes. This implies that the spatial resolution of the solution
is limited by the cell size since the flow variables do not
vary inside a cell [28]
. The FVM is conservative, meaning
that the flux leaving a cell through one of its boundaries
is equal to the flux entering the adjacent cell through the
same boundary. This property makes it advantageous for
problems in fluid dynamics [29]
.
A stationary transport equation involving diffusion and
convection of a general flow variable, variable, φ, can be
written as
(4.2)
Where Γ is the diffusivity and S is a source term that
may depend on . By using the FVM, this equation can be
written in discrete form as
(4.3)
Where
(4.4)
In these equations, where the summations run over all
the nearest neighbors of each cell, φP is the value of the
flow variable in the present cell and φnb are the values
of the flow variable in the neighboring cells. SU and SP
are the constant and flow variables depending on parts
of the source term, respectively. Furthermore, aP is the
discretization coefficient associated with the present
cell, and are discretization coefficients describing the
interaction with its neighboring cells. The discretization
coefficients depend on the discretization schemes used to
approximate the values of the flow variables on the cell
boundaries, also known as cell faces. By using appropriate
discretization schemes to determine the coefficients, a set
of algebraic equations for the cell values is obtained.
4.2 Turbulence
When a hull is moving through the water, the flow
around the hull is turbulent. In this section, the governing
equations of turbulent flows are presented and turbulence
modelling is explained.
4.2.1 Turbulent Flow
Turbulence has no physical definition, but it is char-
acterized as a three-dimensional, irregular flow where
turbulent kinetic energy is dissipated from the largest to
the smallest turbulent scales. On the smallest turbulent
scales, known as the Kolmogorov scales, the energy is
dissipated into heat due to viscous forces. Since turbulence
is a dissipative phenomenon, energy must be continuously
supplied to maintain a turbulent flow.
Analytical solutions to the Navier-Stokes equations
only exist for a limited number of simple cases such as
laminar flow between flat plates. For turbulent flows
in engineering applications, analytical solutions do not
exist and the Navier-Stokes equations must be treated
numerically. If they are solved using direct numerical
simulation (DNS), the velocity field of the flow is
obtained. However, since turbulence occurs on a wide
range of time and length scales, DNS requires very high
temporal and spatial resolutions to capture all the details
of the flow. Thus, DNS is very computationally expensive
and time-consuming which limits the method to special
applications such as academic research or simulation of
simple flows.
4.2.2 Turbulence Modelling
The most common way of treating turbulence is to use
turbulence models in which the turbulent features of the
flow are not resolved in time. By performing Reynolds
decomposition, the instantaneous velocity and pressure
can be decomposed as
(4.5)
Where and denote the time-averaged quantities
while Ui and p are the fluctuating components of the
velocities and the pressure. By inserting the Reynolds
decomposition into the Navier-Stokes equations given
in equation (4.1), the Reynolds averaged Navier-Stokes
(RANS) equations are obtained. These are written as
(4.6)
It can be noted that the RANS equations are very
similar to the Navier-Stokes equations except for the
additional term including uiuj, referred to as the Reynolds
stress tensor. If the Reynolds stress term is modelled,
the RANS equations describe the time-averaged flow
quantities which require substantially less computational
resources in comparison to DNS.
A common approach for modelling the Reynolds stress
tensor of the RANS equations is to use the Boussinesq
approximation. In this assumption, the Reynolds stress
tensor is modelled as a diffusion term by introducing a
turbulent viscosity, νt, according to
(4.7)
In this equation, δij is the Kronecker delta which
assumes a value of 1 if i = j and 0 otherwise, and k is the
turbulent kinetic energy defined as
(4.8)
By using a model to describe how the turbulent visc-
osity depends on the flow, the RANS equations can be
solved. The so-called two-equation turbulence models, such
as the k-ε model and the k-ω model, use two additional
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transport equations to describe the turbulent viscosity.
They are referred to as complete models since they allow
the turbulent velocity and length scales to be described
independently [30]
.
4.2.3 Turbulence Model Standard k-ɛ
The stander k-ɛ model reported by Launder et al., to
obtain turbulent viscous using transport equation for
turbulence kinetic energy k
(4.9)
And transport equation for dissipation rate ɛ
(4.10)
To obtain
(4.11)
Where
, and are model constants
and are variable coefficient for the model
Pk is the production due to mean velocity shear
Table 1 shows the model coefficients improved during the
time by a group of scientists.
Table 1. model coefficients improve with time
model years C1 C2
Launder and Jones 1972 1.55 2 1.3 1 0.9
Sharma and
Launder
1974 1.44 1.92 1.3 1 0.9
Spalding and
Launder
1974 1.44 1.92 1.3 1 0.9
The turbulence k-ɛ has a reasonably accurate and com-
putational cost per iteration compare with most anther
turbulence models, resulting in that it used the engineering
turbulence model for industrial applications. There is
constrain for turbulence model at a wall, strong separation,
curvature and large streamline for that must be using the
wall function.
4.2.4 Turbulence Model k-ω
The turbulence model k-ω reported by Wilcox [31]
. It has
a transportation equation for kinetic energy k and specified
dissipation ω, this specific dissipation has a relation to
dissipation ɛ according to
(4.12)
(4.13)
And transport equation for
(4.14)
To obtain
(4.15)
Where
β*
, , σω, Cω1, and Cω2 are model constants.
The merits of this turbulence model have good work
near the wall and low turbulent regions [29]
. Hence, it is
valid too in the regions of turbulent Reynolds number
locale near to the wall, lead to that the transport equations
can be utilized within the entire stream space. The
disadvantage of the k-ω model is that the results are
sensitive to the choice of boundary conditions and initial
conditions.
4.2.5 Turbulent Model SST
Shear stress transport (SST) model described by
Menter. This model is a hybrid turbulence model to get
advantages k-ɛ and k-ω turbulent models. Figure 16 shows
the Zonal work turbulence model k-ɛ k-ω around the
flat plate, the first model applies in the wake and outward
stream domain. Other models work around the wall it
means sub and log layer.
Figure 16. Zonal work turbulence model k-ɛ k-ω on a
flat plate [32]
According to a combination between k-ɛ and k-ω tur-
bulence models, Transportation equation blended of them
to obtain by
(4.16)
The SST k-ω model has shown good performance
for many types of complex flows, such as inflows with
adverse pressure gradients and separating flows. It has
been recognized for its good overall performance [33]
and it is the most commonly used turbulence model for
simulations of ship hydrodynamics. A drawback needs
a lot of time to solve problems combers with the k-ɛ
turbulence model.
4.3 Geometry of Planing Hull
The model employed in this study is the same as the
one used in the Morabito experiment [34]
whose model
surface is shown in Figure 17. The model is a box shape
whose dimensions are (length 914 mm, beam 183 mm,
depth 102 mm ) with groove (9 mm high × 6 mm deep)
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around the model. It is located at 9 mm above the bottom.
a) Experimental model (Morabito, 2013)
b) Numerical model
Figure 17. Planing hull model a) Experimental model
(Morabito, 2013) b) Numerical model
The test was adopted by moving the model through the
channel at a constant sinkage and trim by aft is equal to
6 degrees. This angle raises the bow of the hull above
the waterline leads to canceling the bow effect. The
hull model is examined in calm water at a range of
speed from 0.3 m/s to 3.7 m/s for water depths 0.5b, 0.75b,
3b, 8b.
The objectives of the Morabito experiment are to
measure the tangential force and normal force on the
bottom of the hull separately using a dynamometer, also
it’s calculated; the change in wetted chain length (LC),
and transom ventilation (YK) at all range of speed.
For that, the hull is divided into station and water line
shown in a.
4.4 Planing Hull Computational Domain
Due to the symmetry of the hull, only half of the com-
putational domain is represented in the CFD simulations
of this study with dimensions shown in Figure 18. The hull
is implemented with a fixed trim of 6 degrees and fixed
heave giving a transom draft of 0.05673 m, such as that in
the experimental work. The study is carried out to simulate
a shallow channel whose water depth is 0.1 L and width is
1.3 L. The reference point of the computational domain is at
G= (0, 0, 0). Boundary conditions imposed on the numerical
domain are shown in Table 2. The air-water flows through
the shallow channel from inlet to outlet about the hull. These
investigations cover a range of speeds from 0.3 m/s to 3.7
m/s. This range includes the three regions of the subcritical,
critical, and supercritical speeds. Also, when the analysis
for deep water, the high seabed equal to 2.46 L. This height
ensures no effect for seabed on the hull resistance.
Figure 18. Dimension and boundary conditions of the
shallow domain
Table 2. boundary conditions details
Position type boundary condition
Boat No-slip wall
Inlet velocity inlet
Outlet static pressure outlet
Top free slip wall
Side free slip wall
Bottom free slip wall
Symmetry - symmetry
In this investigation, was selected the k-ɛ turbulence
model depending on previous work [12]
. At the critical
speeds, when the ship velocity is equal to the velocity of
the wave in a shallow channel, the solitary wave will be
established every some time. The solitary waveform is a
wave single crest that moves forward through the shallow
channel. Hence, it’s required transient analysis at critical
speed as shown in Table 3. The analysis at subcritical
speeds and supercritical speeds are steady with time
shown in Table 4.
Table 3. analysis setting at critical speeds
analysis type transient
number of element 6000000
turbulent model k-ɛ
Total time 25 sec
time step 0.2 sec
Table 4. analysis setting at subcritical and supercritical
speeds
analysis type Steady-state
number of element 6000000
Residual e^-5
Max iteration 10000
turbulent model k-ɛ
4.4.1 Open Water Domain Dimension
An open water analysis needs to make some changes
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while the surface, curve, and undersea refinements will be
added in the overset one. Another refinement is added in
the background domain, to comply with the requirement
of the same cell size in the area around the boundaries
of the overset domain. This is done by inserting in the
background domain a box refinement around the overset
domain with a target cell size of 0.04 m. Figure 22 shows
tetrahedral mesh around the two domains and presium
layer around the hull.
a) Tetrahedral mesh all over the domain
b) Presium layer around the hull
Figure 22. overset mesh generation 1.6 million mesh
element
4.6 Mesh Dependence Study
The one-domain mesh generation gives a tremendous
number of elements when the change element size. To
improve the results a grid dependence study has been
made for three generations of mesh 3, 6, and 9 million
elements. The grid which has 6 million elements give the
best results for all range of speeds and suitable time of
analysis. Table 5 shows the error of numerical results for
three mesh element comber with experimental resistance
and lift force. The minimum error can be founded for the
last speed in the range of critical speed 1.2 m/s achieved
by 6 million elements.
4.7 Solution
When achieved convergence criteria for total resistance
and lift force, the analysis was completed to solve
the problem. Figure 30 explains the value of total
resistance, lift force, and trim moment in the critical
speeds range.
4.8 Post-processing
After the analysis was finished, the post-processing
tool was utilized to describe the contour in the waterline,
dynamic pressure contour on the hull, and the relation
between time and resistance or force or trim moment.
5. Results and Discussion
This study predicted total resistance, generated wave
pattern and, lift force of a planing hull model moving in a
shallow channel over three regions of speed, (subcritical,
critical, and supercritical). Numerical results were vali-
dated by comparison with experimental data available in
the literature [14]
.
5.1 Comparison of Numerical and Experimental
Results
This part of the thesis compares to available exper-
imental results; wetted length, dynamic normal force
over displacement, and lift force coefficient with the
numerical results. Notice that, at the critical speed range
the value of results fluctuates. For that, the mean value
was obtained numerically and compared with the mean
value experimentally. The mean value of results can be
calculated as
(5.1)
Where
Xm = mean result at a critical speed
n = number of results
Xk = results at a critical speed.
Furthermore, the percentage of error calculated from
this equation:
(5.2)
Where
Vex = experimental value
Vnu = numerical value
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Table 5. mesh dependence study for speed 1.2 m/s
Force on hull
(N)
Experimental
(N)
3 Million Elements
Error
(%)
6 Million
Elements
Error (%)
9 Million
Elements
Error (%)
Total Resistance 4.80 6.64 38 6.08 27 6.33 32
Lift Force 41.75 58.98 41 56.03 34 58.00 39](https://image.slidesharecdn.com/volume3issue1january2021-231114030958-3577dca2/75/Sustainable-Marine-Structures-Volume-03-Issue-01-January-2021-51-2048.jpg)
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Sustainable Marine Structures | Volume 03 | Issue 01 | January 2021
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number’s range (Fh=0.84 to Fh=1.27). After that, there is
a slight drop in values and then they increase gradually
over the supercritical Froude number’s range (Fh=1.37
to Fh=3.9). Very good agreement between the two
curves is observed over –almost- the whole range of
Froude numbers albeit, the error increases at Froude
number close to the peak. The total average error
between the numerical and experimental total resistance
is no more than 8%. The maximum error is observed at
the maximum critical Froude-depth number of 1.27 and
is equal to 26%.
Figure 25. Experimental and numerical total resistance
5.1.5 Hydrodynamic Lift Force LF
A comparison between the numerical and experimental
total lift force is shown in Figure 26. In general, the
lift force decreases slightly over the supercritical range
(from Fh=0.32 to Fh=0.63). There is a numerical over
prediction of the lift force in this range. However, the lift
force increases rapidly over the critical speed range (from
Fh=0.84 to Fh=1.27). Subsequently, the value of the lift
force rises gradually over the supercritical range. The total
average error equals 7%, while the maximum error is 34%
at the maximum critical Froude-depth number. For the
whole range of speeds, very good agreement is observed
between the numerical and experimental lift force except
the maximum critical speed of 1.2 m/s.
Figure 26. Experimental and numerical Lift force
5.1.6 Wave Pattern
The numerical and experimental wave pattern is
similar at speed 0.3 m/s as shown in Figure 27. The free
surface deformation at the displacement speed of 0.3m/
s is not significant. In the low-speed region, there is
no high deformation at the hull side, the wetted chine
experimentally and numerically equal to 558.8 mm and
620.9 mm sequentially. Also, there is partial ventilation
at transom equals 2.12 mm experimentally and 3.23 mm
numerically.
a) Experimental Wave pattern [34]
b) Numerical wave pattern
Figure 27. Wave pattern comparison at speed of 0.3 m/s
Figure 28 shows a similarity in the generated wave
pattern numerically and experimentally at a speed of
1.8 m/s. There is a high deformation on the free surface
at the planing speed of 1.8 m/s. While the waves about
the hull side increase in height leading to an increase in
wetted chine equal to 635.42 mm numerically and 609.6
mm experimentally, further the free surface drops at the
transom. For the numerical and experimental generated
wave pattern, high deformation occurs on the free surface and
full ventilation at transom equal to 56.73 mm experimentally
and 59.03 mm numerically.
a) Experimental wave pattern [34]
http://dx.doi.org/10.36956/sms.v3i1.414](https://image.slidesharecdn.com/volume3issue1january2021-231114030958-3577dca2/75/Sustainable-Marine-Structures-Volume-03-Issue-01-January-2021-53-2048.jpg)
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Sustainable Marine Structures | Volume 03 | Issue 01 | January 2021
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Also, there is a hollow that causes the total resistance to
decrease because the crest cancels the trough in the wave
system. Secondly, the total resistance in shallow water
is rising rapidly in the critical speed period (0.8---1.2 m/
s). The maximum difference between total resistance
in shallow water and total resistance in deep water is
equal to 43% at speed 0.9 m/s. The total resistance in
the supercritical speed range increases dramatically with
speed increase. Lastly, the total resistance in the shallow
channel is much higher than the total resistance in open
water.
Figure 33. Total resistance of planing hull in deep water [35]
Figure 34. Comparison between the total resistance in
shallow water channel and the total resistance in deep
water
6. Conclusions and Recommendations
In this chapter, the conclusion, and recommendation of
the results from the numerical simulations of the prismatic
planning hull when moving in various waterways, is
presented.
6.1 Conclusions
In this thesis, the RANS equations are solved by ANSYS-
CFX code to simulate a small high-speed hull form moving
in a shallow channel and open water. The total resistance and
wave pattern of the planing hull model at three regions of
speed (subcritical, critical, and supercritical) moving in
the shallow channel have been numerically simulated.
• In the shallow water channel, the total average error
equals 7% for numerical lift force, 8% for numerical total
resistance compared with available experimental results.
The numerical analysis well captured the wave pattern.
The numerical results give good agreement over the whole
range of speeds with the experimental results except at
the maximum critical speed of 1.2 m/s which resulted
in an error equal to 34% for lift force and 26% for total
resistance.
• In the current study, the steps of the solitary wave
formulation have been described at critical speeds. The
amplitudes of the solitary waves were determined at the
critical speeds of 0.8, 0.9, 1, 1.1, and 1.2 m/s where the
amplitudes were found equal to 0.0133, 0.0263, 0.035,
0.0414, and 0.0596 m respectively.
• The amplitude of the solitary wave increases when-
ever there is an increase in the critical speeds. Also, this
investigation defined the locations of the solitary wave
formulation. Solitary wave formulates in front of the hull
at the lower range of critical speed. However, at the higher
range of critical speed, it formulates on the hull.
• The solitary wave formation increases the wetted
surface area and the free surface deformation. Also,
causes fluctuation in the trim moment, and lift force on
the planing hull depends on the location and amplitude of
the solitary wave. The total resistance on the hull in the
http://dx.doi.org/10.36956/sms.v3i1.414
(a) Wave elevation in a deep water (b) Wave elevation in a shallow water
Figure 32. wave systems comparison between a shallow channel and open water at speed 1m/s](https://image.slidesharecdn.com/volume3issue1january2021-231114030958-3577dca2/75/Sustainable-Marine-Structures-Volume-03-Issue-01-January-2021-57-2048.jpg)
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Sustainable Marine Structures | Volume 03 | Issue 01 | January 2021
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shallow channel is higher than the total resistance in open
water. The maximum difference is 43% which takes place
at a critical speed of 0.9 m/s.
In conclusion, the worst effect on the planing hull in
shallow channels occurs at the critical speed range, where
solitary wave formulates. So boat drivers must avoid
sailing at a critical speed range.
6.2 Recommendations
The future working which can be recommended:
• Further study for critical speed ranges and behavior
of hull motion especially the last speed in the range.
• Using various mesh strategies and different software
for this investigation to do more verification of results.
• Additional research concerns the effect of channel
dimensions and vessel shape on the formation of a solitary
wave at a critical speed.
• Research on how to apply solitary wave energy in
useful engineering applications.
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http://dx.doi.org/10.36956/sms.v3i1.414](https://image.slidesharecdn.com/volume3issue1january2021-231114030958-3577dca2/75/Sustainable-Marine-Structures-Volume-03-Issue-01-January-2021-58-2048.jpg)
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2018.
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Nomenclature
Latin Symbols
L Length of planing hull (m)
B Beam (m)
h Depth of water (m)
U Speed of the vessel (m/s).
g Gravitational acceleration (m/s2
)
RT Total resistance (N)
RV Viscous resistance (N)
RA Air resistance (N)
RS Spray rail resistance (N)
RM Wave making resistance (N)
LF Lift force (N)
Fn Froude number
Cv Beam Froude number,
Fh Depth Froude number
P Pressure (N/m2
)
tw Sheer stress (N/m2
)
Cf Frictional resistance
V Speed of a wave (m/s)
Lc Chine wetted length (m)
LK Length of the keel (m)
N Dynamic normal force (N)
CLβ Lift coefficient
CL0 Lift coefficients atβ = 0◦
FLβ Lift force (N)
FL0 Lift force at β= 0◦
Greek Symbols
α Kliven angle (degree)
β Deadrise angle (degree)
ρ The density of the fluid (kg/m3
)
τ Trim angle of planing hull (radian).
v Kinematic viscosity (m2
/s)
λw
Mean wetted length-to-beam ratio, λw =
0.5(LC+LK/B) ,
Taken rang λw ≤ 4.
τdeg The trim angle of the planing
hull (degree) take rang 2◦≤ τdeg≤ 15◦
Abbreviation
FDM Finite Different Method
FVM Finite Volume Method
FEM Finite Element Method
BEM Boundary Element Method
2D Tow dimension
3D Three dimensions
VOF Volume of Fluid
CFD Computational Fluid Dynamic
RANSE Reynolds Navier Stokes Equation
ACV Air Cushion Vehicles
SES Surface-Effect Ships
SWATH Small water-plane-area twin-hull
ITTC International Towing Tank Conference
PDE Partial Differential Equation
DNS Direct Numerical Simulation
SPH Smoothed Particle Hydrodynamics
http://dx.doi.org/10.36956/sms.v3i1.414](https://image.slidesharecdn.com/volume3issue1january2021-231114030958-3577dca2/75/Sustainable-Marine-Structures-Volume-03-Issue-01-January-2021-59-2048.jpg)
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Sustainable Marine Structures | Volume 03 | Issue 01 | January 2021
This document provides information about the editorial board of the journal "Sustainable Marine Structures". It lists the editor-in-chief, associate editors, and 34 editorial board members along with their affiliations. It also provides brief information about the journal, including its ISSN number, volume and issue details. The contents section lists 5 articles in the current issue along with their titles and authors.
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Sustainable Marine Structures | Volume 03 | Issue 01 | January 2021
- 2. Editorial Board Editor-in-Chief Associate Editors EditorialBoard Member Prof. Surendran Sankunny Indian Institute of Technology Madras, India Dr. Erkan Oterkus University of Strathclyde, United Kingdom Junnan Cao, United States Shuhong Chai, Australia Wen Deng, United States Omar Y. El Masri, United States Mohammad Heidarzadeh, United Kingdom Ri Na, United States Selda Oterkus, United Kingdom Weichao Shi, United Kingdom Decheng Wan, China Zhiming Yuan, United Kingdom Chungkuk Jin,United States Sardono Sarwito,Indonesia Can Eytemiz,Turkey Chia-Cheng Tsai,Taiwan, Province of China Mujeeb Ahmed Mughadar Palliparambil,United Kingdom Debajit Datta,India Noora Barzkar,Iran Seyed Majid Mosaddad,Iran Manhar R. Dhanak, United States Alberto Francescutto, Italy Mohammad Rafiqul Islam, Bangladesh Ann Rigmor Nerheim, Norway Eugen Victor-Cristian Rusu, Romania Shaopin Song, United States Bing Wang, United Kingdom Peace Nwaerema, Nigeria Fuat Kara,United Kingdom Xiangyuan Zheng,China Ajaykumar Ramdas Kambekar,India Saleem Mustafa,Malaysia Dr. Do Duc Luu,Vietnam Durga Prasad Behera,India Lan Dinh Tran,Viet Nam
- 3. ISSN: 2661-3158 (Online) Volume3 Issue 1 January 2021 SUSTAINABLE MARINE STRUCTURES Editor-in-Chief S. Surendran IIT Madras, India
- 4. Volume 3 ｜Issue 1 ｜ January 2021 ｜ Page 1-55 Contents ARTICLE Analyzing Potential Water Harvesting from Atmosphere in Near Coastal Area Ida Bagus Mandhara Brasika Putra Bagus Panji Pamungkas Non-linearity Analysis of Ship Roll Gyro-stabilizer Control System Sathit P. Chatchapol C. Phansak I. Application of Fourth Industrial Revolution Technologies to Marine Aquaculture for Future Food: Imperatives, Challenges and Prospects Sitti Raehanah M. Shaleh Rossita Shapawi Abentin Estim Ching Fui Fui Ag. Asri Ag. Ibrahim Audrey Daning Tuzan Lim Leong Seng Chen Cheng Ann Alter Jimat Burhan Japar Saleem Mustafa A CFD Study of the Resistance Behavior of a Planing Hull in Restricted Waterways Ahmed O. Elaghbash 8 1 22 32 Sustainable Marine Structures
- 5. 1 Sustainable Marine Structures| Volume 03 | Issue 01 | January 2021 Distributed under creative commons license 4.0 DOI: http://dx.doi.org/10.36956/sms.v3i1.354 Sustainable Marine Structures http://ojs.nassg.org/index.php/sms/index ARTICLE Analyzing Potential Water Harvesting from Atmosphere in Near Coastal Area Ida Bagus Mandhara Brasika1,2* Putra Bagus Panji Pamungkas2 1. Department of Marine Sciences, Universitas Udayana, Jl. Raya Kampus Unud, Jimbaran, Kuta Selatan, Badung, Bali, 80361, Indonesia 2. Center of Remote Sensing and Ocean Science (CReSOS), Universitas Udayana, Jl. P.B. Sudirman, Denpasar, Bali, 80234, Indonesia ARTICLE INFO ABSTRACT Article history Received: 26 April 2021 Accepted: 18 May 2021 Published Online: 30 May 2021 Water is a basic need. However there are many near coastal regions which have very limited access to fresh water. The water in area close to coastal is mainly affected by ocean, indirectly by weather/climate conditions and directly from seawater intrusion. While abundant fresh water is actually available in the atmosphere in the form of moisture. Recent technology, such as Atmospheric Water Generator (AWG), is a possible solution to gain water from our atmosphere. However, comprehensive study is need- ed to understand the potential water harvesting in our atmosphere. Here, we examine the water availability in the atmosphere based on several parameters like temperature and humidity. The data are collected from observation using WS1040 Automatic Weather Station in a year of 2020 with a half-hour interval. Then, we calculate the availability of water con- tent during each season, especially in dry conditions. We also simulate the water harvesting to fulfil daily basic need of fresh water. The atmospheric parameters have shown a monsoonal pattern. Water content decrease in atmosphere during the dry season but the water deficit occurs after the dry season. Although water harvesting able to supply daily freshwater need, it is not recommended to be a single source as it requires massive water storage and high-efficient AWG. Keywords: Water scarcity Intrusion Water vapor density Atmospheric water generator *Corresponding Author: Ida Bagus Mandhara Brasika, Department of Marine Sciences & Center of Remote Sensing and Ocean Science (CReSOS), Universitas Udayana, Jl. Raya Kampus Unud, Jimbaran, Kuta Selatan, Badung, Bali, 80361, Indonesia; Email: mandharabrasika@unud.ac.id 1. Introduction Water scarcity or drought is a growing issue in many regions. There is no standard for drought definition [1] , but in general drought is the dry condition longer than nor- mal and causes the availability of water below the need [2] . There are more than 4.3 billion people or 71% of the global population living under conditions of moderate to severe water scarcity at least 1 month of the year [3] . The report from the World Economic Forum stated that water crises as the largest global risk in terms of potential im- pact [4] . The water scarcity might be caused by the high demand or limited access of water. In the case of Bali, the water crisis is dominantly caused by the conflict of different user groups [5] . The factor of growing tourism has a significant role. However, some areas like Karangasem (East Bali) have limited ac-
- 6. 2 Sustainable Marine Structures| Volume 03 | Issue 01 | January 2021 Distributed under creative commons license 4.0 cess to water [6] due to their geographical condition. Due to several research, 260 of 400 rivers in Bali had run dry and the largest lake had dropped 3.5 metres [7] . The climate has worsened the condition. Some climate variability like El Nino Southern Oscilla- tion (ENSO) has a significant role. Indonesia precipitation is strongly correlated with ENSO variations in the Pacific basin [8] . There are 43 drought events in Indonesia during 1884-1998, only 6 of them are not correlated with El Nino [9] . Meteorological role is more common in Bali during moderate and strong El Nino [10] . Rainfall pattern in Bali is categorized as the Monsoonal season which has the driest and longest dry season in Indonesia, compared to other types of season. The minimum mean of rainfall might reach 100 mm/month with the driest months between July and September [11] . El Nino has a significant impact on dry conditions in Bali, mainly between June-October [12] . El Nino Modoki (tripole mode of El Nino) has decreased rainfall in Bali for 8-16%[13] . Bali is a relatively small island which most of their population lives near the coastal area. The near coastal region with a high population has been threatened by salt- water intrusion. This characteristic can be found in south Bali such as Denpasar, Kuta, Jimbaran and others. For example in Jimbaran (our observation station located) has several coastal close by and the closest one is less than 3 km away. The salt groundwater in this region is mainly caused by seawater and hydrogeochemical influence [14] . In other similar near coastal region with a high popula- tion like Semarang city, salt water intrusion affects 3,44 km region from coastline [15] . However, this region might change over time depending on several factors such as aquifer withdrawal, hydraulic properties and the confining unit of coastal area[16] . There are many solutions offered to combat this water crisis. Some like rainwater harvesting, greywater reuse and solar desalination [17,18,19] . Other potential solution is harvesting the water directly from the atmosphere as our air contains lots of water. On land surface, satellite can detect the amount of water in our air is about 3,42 g/cm2 , with standard deviation 1.82 g/cm2 [20] . However, we need tools to collect the water from the atmosphere, it is known as Atmospheric Water Generator (AWG). AWG has a sim- ilar principle with Refrigerator and Air Conditioner which is cooling through evaporation [21] . But before developing AWG, we have to understand our weather condition. How much the real water content in our area. When is the op- timum condition for water harvesting. In this research we observe several atmospheric parameters. Then calculate water availability. Lastly it is simulated to model the po- tential of water harvesting to fulfil daily water needs. 2. Data and Method 2.1 Atmospheric Observation using WS1040 In this research we observe the atmospheric condi- tion in our station at the Faculty of Marine Science and Fisheries, Udayana University. The observation utilizes Automatic Weather Station (AWS) as a data collector. The AWS is WS-1040 which is produced by Shenzen TianKan Electronic Technology Co., Ltd. WS-1040 is a series of wireless weather station with solar power and PC link. This can be seen in Figure 1 [22] . Figure 1. Installation of WS1040 Weather Station on Udayana University With this equipment, we can observe several atmo- spheric parameters such as Temperature, Humidity, Pressure, Wind Speed, Wind Direction and Rainfall in 30 minutes timestep. The WS-1040 is placed in the open-air area with a radius of minimum 30 meters to the closest building. This is to reduce the bias of data caused by the effect of building or other possible disturbance such as trees, animals and humans. Figure 2 shows the location of the weather station. It is located in the open area of Faculty Marine Sciences and Fisheries, Udayana University. The closest coast is less than 3 km away and there are many beaches in the radius 5 km. Thus, the potential of seawater intrusion is high and atmospheric condition is mainly affected by ocean. Figure 2. Location of Weather Station: Faculty of Marine Sciences and Fisheries, Udayana University DOI: http://dx.doi.org/10.36956/sms.v3i1.354
- 7. 3 Sustainable Marine Structures| Volume 03 | Issue 01 | January 2021 Distributed under creative commons license 4.0 The observation was started in November 2019. How- ever, the data that will be used in this research is from 01 January 2020 to 31 December 2021. We only focus on 3 parameters, Station Pressure (mBar), Ambient Tempera- ture (C) and Relative Humidity (%). As they are consid- ered in optimization design of Atmospheric Water Gener- ator [23,24,25] . The daily, monthly and seasonally pattern of these at- mospheric parameters will be observed to gain a general understanding of the condition in our station. For the sea- son, we divide it into 2 season, dry and rainy. This is due to the classification of Indonesia’s rainy season. Bali is located in the area with Monsoonal season [11] . Dry season is in April-September, while the rainy season is Octo- ber-March. 2.2 Water Content at Atmosphere To measure the amount of water in the air, we calculate it from several atmospheric parameter such as temperature and relative humidity. From research by [26] , the absolute humidity is calculated as below: ρv = e M R T a w × k ea and es is given by: e RH e a s = × es = × 0.611 exp( ) c T b T + × Where: ρv is absolute humidity or vapor pressure (g/ m3 ), ea is vapor pressure (kPA), es is saturation vapor pressure (kPA), Mw is molecular weight of water (g/mol) equal to 18.02 g/mol, R is universal gas constant (J/ mol K) equal to 8.31 J/mol K. T is temperature in Celcius (o C) while Tk is in Kelvin (o K). RH is relative humidity (%). b and c both are constant, b = 17.502 and c = 240.97 for liquid. 2.3 Model of Water Surplus/ Deficit By calculating the amount of water content in the at- mosphere, we can address fluctuation of potential water harvesting. However, this information is insufficient to explain the possibility of water harvesting from the atmosphere to fulfil the daily water need. We have to consider the minimum need and optimum need of water per person. We model this condition by fitting the gap between water harvesting and water need in daily time-series. The surplus and deficit of water can be seen on our model. We simulate the extra water of each day being stored in water storage. So, the surplus/deficit water is calculated based on cumulative sum. The surplus day is when the cumulative sum is positive (above 0) and the deficit is negative (below 0). In this model we assume there is 100% efficiency and 10.000 m3 of air is pro- cessed in a day. The sum of surplus and deficit is also calculated to evaluate the annual condition. This can address the strategy of water storage. 3. Result and Discussion 3.1 Atmosphere Variability a. Daily and Monthly Atmosphere Variability First, we want to understand the general variability of several atmosphere parameters during our observation time. Figure 3 shows the daily and monthly timeseries of Temperature, Humidity and Absolute Pressure in 2020 at the Faculty of Marine Sciences and Fisheries Campus using weather station WS1040. The daily mean illustrates clearly the oscillation of its variability. The monthly timeseries can show the general condition but fail to detect the detail. For example, humidity between October-De- cember is low in monthly average but if we see the daily average it increases in general. This is because during this month the humidity is more unstable which causes more significant changes day by day. DOI: http://dx.doi.org/10.36956/sms.v3i1.354
- 8. 4 Sustainable Marine Structures| Volume 03 | Issue 01 | January 2021 Distributed under creative commons license 4.0 Figure 3. Daily and Monthly timeseries of Temperature, Humidity and Absolute Pressure from 01 January 2020 to 31 December 2020 Temperature has two peaks in April and November, with the lowest point in August. In general during the dry season (April-September), the temperature is domi- nantly low. Compared with the dry season, it has higher unstable monthly temperature. This is very clear when November mean temperature reaches more than 29o C, then it drastically drops to less than 27,5 o C in Decem- ber. For humidity, it starts to decrease in April (dry sea- son) and remains low until December. So, it is generally low humidity during the dry season. The absolute pres- sure has an opposite pattern. It is dominantly high during the dry season between April-September. However, these three patterns have shown the similarity of unstable conditions between October-December. It is clear when we compare daily average and monthly average. While the monthly average is going down, the daily average is generally increasing. b. Day-night Mean during Rainy Season To get a deeper understanding of when is the optimum time to generate water from the atmosphere. Here we compare the day-night mean of atmosphere parameters in the rainy and dry season with a 30 minutes interval. We calculate the mean of parameter at the same time during a season. For example, the mean temperature at 7 a.m. in the morning in each day during the rainy season. The dry season will be discussed in the next section. Day-night mean in the rainy season (October-March) can be seen in Figure 4 as below: Figure 4. Day-night mean variability of Temperature, Humidity and Absolute Pressure during Rainy season General day-night variability is clearly shown for all parameters in the rainy season. Temperature and Humid- ity have an opposite pattern. While temperatures increase between 08.00 to 14.00, humidity decreases exactly at the same time. Then temperature decreases from 14.00 to 19.00 and humidity increases. At night (19.00-08.00), both temperature and humidity day-night mean are con- stant. The absolute pressure is oscillating the peak around 09.00 and 23.00, the lowest point around 16.00. c. Day-night Mean during Dry Season Figure 5 shows day-night mean during the dry season between October-March. In general there are no signifi- cant differences between the rainy season and dry season. The cycle of increasing and decreasing temperature ap- pears between 07.00-19.00. While the humidity shows the opposite pattern with temperature. Figure 5. Day-night mean variability of Temperature, Humidity and Absolute Pressure during Dry season DOI: http://dx.doi.org/10.36956/sms.v3i1.354
- 9. 5 Sustainable Marine Structures| Volume 03 | Issue 01 | January 2021 Distributed under creative commons license 4.0 There are slight differences in the ranges. For ex- ample, in dry seasons humidity might occur less than 40%. Temperature, humidity and absolute pressure are also more unstable in the dry season compared with the rainy season. There is a typical stable time for the pa- rameter. Temperature and Humidity are relatively sta- ble from 09.00 to 10.00. The absolute pressure is more stable around 12.00. 3.2 Optimum Water Generation By calculating absolute humidity, we get the amount of water in every cubic of air in the atmosphere. Although not all 100% water content can be extracted into fresh wa- ter due to the limitation of technology, we can still under- stand its potential by analysing the variation of absolute humidity. Here Figure 6 simulates the daily mean of water content of every 10.000 m3 air for each month. As water is a daily need, we need water availability on a daily basis. Here we plot daily mean of water content, based on abso- lute humidity calculation, in Figure 6. Figure 6. Daily mean of absolute humidity Here we can see the detailed daily water content. Al- though the monthly mean is high, it does not mean that the water is available in the very high concentration every day. For example, between January and March the water content is changing drastically day-by-day. The highest reaches almost 150 litres per day, while the lowest is less than 25 litres per day. In the dry season, the variation of water content is lower. It is constant around 20-50 litres per day. To collect water from the atmosphere, AWG (Atmo- spheric Water Generator) is the tool. There are many types of AWG. However, the efficiency of water generation is mainly caused by 3 factors, Temperature, Humidity and Flow rate. For example, AWG with a thermoelectric cool- ing method able to produce 11-25 gram/hour [27] . The wa- ter generation increases with humidity and air flow rate, optimum in 90% RH and 30 m3 /h flow rate [28] . 3.3 Daily Fresh Water Simulation To fulfil their fundamental water need, humans require about 50 litres of water per person per day, although in humid climates humans can survive with 10 to 20 litres per person per day [29] . In Figure 7, we illustrate the gap between daily water need (50 litres/day/person) and daily water supply/availability. We assume there is water stor- age which is able to store all surplus water in previous days. So we can understand the surplus (positive) and the deficit (negative) of water on a daily basis. Figure 7. water surplus/deficit simulation Between January and May, the water supply is high- er than water demand. The excess water can be stored in water storage. The minimum capacity of water stor- age needed is 2.000 litres which is seen from the peak of the graph. Then in the period May-November, the water supply decreases below the daily fundamental water need. This caused a drop in water storage until the water deficit in September-November. The water deficit is not in the dry season, but months after the dry season. In total, we can extract 14.528 litres of water in a year if we process 10.000 m3 of air every day and gain 100% efficiency. Unfortunately, it is not enough to fulfil water needs of a person in a year, as it still deficit around 871 litres of water. 4. Conclusions Our atmosphere observation with WS1040 automat- ic weather station has shown the variability of several parameters in our location. The data with intervals 30 minutes provide detailed observation. Temperature is more stable during the dry season from April to September which increases periodically every month. While in the rainy season, the unstable condition might be caused by some disturbance such as heavy rain or strong wind. The humidity has the opposite general pat- tern with temperature during the dry season. However in the rainy season, both temperature and humidity are generally high. These two parameters are utilized to calculate the water content in atmosphere. Between January and May is the highest water content in the atmosphere. The daily water content in November-May fluctuates heavily in the day to day basis, while in other months it is consistently low. DOI: http://dx.doi.org/10.36956/sms.v3i1.354
- 10. 6 Sustainable Marine Structures| Volume 03 | Issue 01 | January 2021 Distributed under creative commons license 4.0 The water harvesting potential might reach as high as 100 litres per day. Our model shows that harvesting 100% water from 10.000 m3 air per day is not sufficient to fulfil a person’s fundamental daily water need (50 litres/person/day) be- tween September and November. In the other months, we need water storage with a minimum capacity of 2.000 litres to save the water surplus. However, with this simu- lation, humans in the near coastal region of south Bali are able to survive by harvesting water from the atmosphere although not all of their fundamental daily water needs are fulfilled. The use of water harvesting methods as a single source of water is not recommended. References [1] J. Dracup, "Drought monitoring," Stochastic Hydrol- ogy and Hydraulics 5, pp. 261-266, 1991. [2] R. Nagarajan, "Drought assesment," Springer, Dor- decht-Netherlands, 2009. [3] M. M. Mekonnen and A. Y. Hoekstra, "Four billion people facing severe water scarcity," Science Ad- vance, 2016. [4] World Economic Forum, "Global Risks," World Eco- nomic Forum, Geneva, 2015. [5] S. Straus, "Water Conflicts among Different User Groups in South Bali, Indonesia," Human Ecology, pp. 69-79, 2011. [6] G. A. Bawantu, I. G. B. S. Dharma and N. N. Puji- aniki, "Potensi Air Permukaan Daerah Aliran Sungai Unda Untuk Memenuhi Kebutuhan Air Domestik dan Non Domestik di Kabupaten Karangasem," Jur- nal Spektran, pp. 38-49, 2018. [7] L. Samsura, "aljazeera.com," 1 12 2019. [On- line]. Available: https://www.aljazeera.com/ news/2019/12/1/bali-the-tropical-indonesian-island- that-is-running-out-of-water. [8] Hendon, "Indonesian rainfall variability: impacts of ENSO and local air-sea interaction," Journal of Cli- mate, pp. 1775-1790, 2003. [9] B. Irawan, "Multilevel impact assesment and cop- ing strategies againts El Nino: case of food crops in Indonesia," Working Papers 32714, United Nations Centre for Allevation of Poverty Through Secondary Crops Development in Asia and the Pacific (CAPSA), p. 105pp, 2003. [10] A. M. Setiawan, W.-S. Lee and J. Rhee, "Spa- tio-temporal characteristics of Indonesian drought related to El Nino events and its predictability using multi-model ensemble," International Journal of Cli- matology, 2017. [11] E. Aldrian and R. D. Susanto, "Identification of Three Dominant Rainfall Regions Within Indonesia and Their Relationship to Sea Surface Temperature," International Journal of Climatology, pp. 1435-1452, 2003. [12] I. W. Nuarsa, I. W. S. Adnyana and A. R. As-syakur, "Pemetaan daerah rawan kekeringan di Bali-Nusa Tenggara dan hubungannya dengan ENSO menggu- nakan aplikasi data penginderaan jauh," Jurnal Bumi Lestari, pp. 20-30, 2015. [13] I. B. M. Brasika, "Ensemble Model of Precipitation Change Over Indonesia Caused by El Nino Modoki," Journal of Marine Research and Technology, pp. 77- 81, 2021. [14] M. Bahar, R. Mezbaul and M. Salim, "Hydrochemi- cal characteristics and quality assessment of shallow groundwater in a coastal area of Southwest Bangla- desh," Environment Earth Science, pp. 1065-1073, 2010. [15] N. Rahmawati, J.-F. Vuillaume and I. L. S. Purna- ma, "Sea intrusion in Coastal and Lowland areas of Semarang City," Journal of Hydrology, pp. 146-159, 2013. [16] K. Rotzoll, D. S. Oki and A. I. El-Kadi, "Changes of freshwater-lens thickness in basaltic island aquifers overlain by thick coastal sediments," Hydrogeology Journal, pp. 1425-1436, 2010. [17] I. N. Norken, I. K. Suputra, I. B. N. Purbawijaya and I. B. P. Adnyana, "Rainwater Harvesting for Drinking Water in Bali: Indonesia," Engineering and Technol- ogy Quarterly Reviews, 2019. [18] A. Khatun and M. Amin, "Greywater reuse: a sustain- able solution for water crisis in Dhaka, Bangladesh," in 4th Annual Paper Meet and 1st Civil Engineering Congress, Dhaka, 2011. [19] S. Gorjian and B. Ghobadian, "Solar desalination: A sustainable solution to water crisis in Iran," Renew- able and Sustainable Energy Reviews, pp. 571-584, 2015. [20] N. C. Grody, "Remote Sensing of Atmospheic Water Content from Satellites Using Microwave Radiom- etry," Transactions on Antennas and Propagation, p. 24, 1976. [21] A. Tripathi, S. Tushar, S. Pal, S. Lodh, S. Tiwari and P. R. Desai, "Atmospheric Water Generator," Inter- national Journal of Enhanced Research in Science, Technology and Engineering, 2016. [22] Dongguan Meteorology Electronic Technology Co., Ltd., "meteorologyhk.com," 20 02 2021. [Online]. Available: http://www.meteorologyhk.com/en/dis- playproduct.html?id=2855714227421508#. [23] S. Suryaningsih and O. Nurhilal, "Optimal Design of an Atmospheric Water Generator (AWG) based DOI: http://dx.doi.org/10.36956/sms.v3i1.354
- 11. 7 Sustainable Marine Structures| Volume 03 | Issue 01 | January 2021 Distributed under creative commons license 4.0 on thermo-electric cooler (TEC) for Drought in Ru- ral Area," in AIP Conference Proceeding, Bandung, 2016. [24] K. Pontious, B. Weidner, N. Guerin, A. Dates, O. Pierrakos and K. Altaii, "Design of an Atmospheric Water Generator: Harvesting Water out of Thin Air," in IEEE Xplore, Charlottesville, 2016. [25] T. Anbarasu and S. Pavithra, "Vapour compression regrigeration system generating fresh water from hu- midity in the air," in SEISCON, Chennai, 2011. [26] C. Campbell, "Environmental Biophysics," 19 De- cember 2019. [Online]. Available: https://www. environmentalbiophysics.org/chalk-talk-how-to-cal- culate-absolute-humidity/. [27] S. Liu, W. He, D. Hu, S. Lv, D. Chen, X. Wu, F. Xu and S. Li, "Experimental analysis of a portable at- mospheric water generator by thermoelectric cooling method," in 9th International Conference on Applied Energy, Cardiff, 2017. [28] W. He, P. Yu, Z. Hu, S. Lv, M. Qin and C. Yu, "Ex- perimental Study and Performance Analysis of a Por- table Atmospheric Water Generator," Energies, p. 73, 2019. [29] P. H. Gleick, "Basic Water Requirements for Human Activities: Meeting Basic Needs," Water Internation- al, pp. 83-92, 2009. DOI: http://dx.doi.org/10.36956/sms.v3i1.354
- 12. 8 Sustainable Marine Structures| Volume 03 | Issue 01 | January 2021 Distributed under creative commons license 4.0 DOI: http://dx.doi.org/10.36956/sms.v3i1.316 Sustainable Marine Structures http://ojs.nassg.org/index.php/sms/index ARTICLE Non-linearity Analysis of Ship Roll Gyro-stabilizer Control System Sathit P.1* Chatchapol C.2 Phansak I.3 1. Department of Maritime Engineering, Faculty of International Maritime Studies, Kasetsart University, Chonburi, 20230, Thailand 2. Department of Mechanical Engineering, Faculty of Engineering, Kasetsart University, Bangkok, 10900, Thailand 3. Department of Nautical Science and Maritime Logistics, Faculty of International Maritime Studies, Kasetsart University, Chonburi, 20230, Thailand ARTICLE INFO ABSTRACT Article history Received: 19 January 2021 Accepted: 24 May 2021 Published Online: 30 May 2021 A gyro-stabilizer is the interesting system that it can apply to marine vessels for diminishes roll motion. Today it has potentially light weight with no hy- drodynamics drag and effective at zero forward speed. The twin-gyroscope was chosen. Almost, the modelling for designing the system use linear model that it might not comprehensive mission requirement such as high sea condition. The non-linearity analysis was proved by comparison the re- sults between linear and non-linear model of gyro-stabilizer throughout fre- quency domain also same wave input, constrains and limitations. Moreover, they were cross checked by simulating in time domain. The comparison of interested of linear and non-linear close loop model in frequency domain has demonstrated the similar characteristics but gave different values at same frequency obviously. The results were confirmed again by simulation in irregular beam sea on time domain and they demonstrate the difference of behavior of both systems while the gyro-stabilizers are switching on and off. From the resulting analysis, the non-linear gyro-stabilizer model gives more real results that correspond to more accuracy in a designing gyro-sta- bilizer control system for various amplitudes and frequencies operating condition especially high sea condition. Keywords: Active gyro-stabilizer Twin gyro-stabilizer Ship large roll motion System identification Inverse problems Non-linear damping moment Non-linear restoring moment *Corresponding Author: Sathit P., Department of Maritime Engineering, Faculty of International Maritime Studies, Kasetsart University, Chonburi, 20230, Thailand; Email: sathit.pon@ku.th 1. Introduction Of the six modes of motions of marine vessel, roll motion is the important mode to be realized. It is the greatest rea- son to capsize also affect to operation of crews, passengers comfortable and cargos damage, when a vessel is excited by wave load at high sea. In order to diminish amplitude of roll motion, roll stabilizer system becomes an important role. More than 100 years, many types of roll stabilizer have been engineered by many researchers and designer. The following example, bilge keels, sloshing, sliding weight, gyro, u-tube, sea-ducted, variable angle fins, hydro-foil keel fin and rotating cylinder etc. Since 1995, Chadwick [1] had gathered types of roll stabilizers and it has been classified by control method to be passive and active control. The passive control is the control system does not require any external power source to operate the control device but the active control system does opposite way [2] . Some types of stabilizer are only passive control as bilge keels and sloshing. Some types are only active control such as variable angle fins and ro- tating cylinder. And some types are both as sliding weight,
- 13. 9 Sustainable Marine Structures| Volume 03 | Issue 01 | January 2021 Distributed under creative commons license 4.0 DOI: http://dx.doi.org/10.36956/sms.v3i1.316 u-tube, sea-ducted and gyro. Another way, Haghighi and Jahed-Motlagh [3] have men- tioned classification of system control types that can be clas- sified as either external or internal control systems. The ex- ternal control system is systems generate resisting load (forces and moments) outside a ship hull and the internal control system is systems generate resisting load inside[4] . The prin- cipal advantages of internal systems are not hydrodynamic drag and effective zero forward but it has heavyweight, volume penalty and there are limited in stabilisation capa- bility. The external systems have lightweight but it creates hydrodynamic drag and ineffective at zero forward speed[5,6] . The following above mention and nowadays technology, a gyro-stabilizer become to be an interesting system at present. Because it has a combination of internal and external control system advantages: potentially light weight with no hydrody- namic drag and is effective at zero forward speed. A gyro-stabilizer system is to use resisting moments, which moments are the cross product between angular momentum vector of flywheel and angular velocity vector around precession axis. The resisting moment is applied to vehicle or other to resist external excitation moments that are keeping minimal oscillatory amplitude of rolling vehicle. However, this paper focus on a gyro-stabilizer is applied to small ships. Background stories of gyro-stabilizers, it has been applied to various inventions. The first gyro-stabilizer ap- plication was invented by Brennan [7] . Brennan used twin gyro that it had counter-rotating flywheels to stabilize unstable vehicle (two-wheel monorail car). This invention had similar patents [8,9,10] . More researches, gyro-stabiliz- ers were applied to stabilize to two-wheel vehicles such as a bicycle [11] and motorcycles [12] . In 1996, Brown and his team published about the using gyro-stabilizer that pro- vides mechanical stabilization and steering a single-wheel robot [13] . Another field, NASA used the advantage of gyro-stabilizer to control attitude of large space structures or satellite [14] . In additional, gyro-stabilizers have been applied to maritime field, e.g., autonomous under water vehicle [15,16] , torpedo [17] and free surface vehicle etc. The first record of a gyro-stabilizer in marine vehicles was found accidentally, Howell torpedo, it was installed rapid rotation of fly-wheel. There was 16 inches a steel wheel diameter and was spun up to 16,000 rpm. The torpedo was experimented for locking target on U.S. Navy boat[17] . For the first time of free surface vehicle, a gyro-stabilizer device was passive system that it was utilized to diminish roll motion [18,19,20] . Active gyro-stabilizer systems were developed from passive systems. The first system was proposed by Elmer Sperry in 1908 [21] . Recently, many researchers have proposed related new researches of gyro-stabilizer with novel control methods. Townsend et al. [4] published a new active gyrostabiliser system for ride control of marine vehicle. McGookin et al. [22] published application of MPC and sliding mode con- trol to IFAC benchmark models. Perez and Steinmann [23] demonstrated analysis of ship roll gyrostabilizer control that revisited the modelling of coupled vessel-gyrostabilizer and also describes design trade-offs under performance lim- itation. Haghighi and Jahed-Motlagh [3] proposed ship roll stabilization via sliding mode control and gyrostabilizer. For designing ship roll gyro-stabilizers, a preliminary design is important thing. In order to design it, designers need to know wave loads, ship motion (ship model) and actuator characteristics (gyro-stabilizer model). Almost, modelling of designing uses an equation of motion in lin- ear model that it may not comprehensive mission require- ment, such as large roll motion from both high amplitudes and frequencies of waves. Normally, these have to be non-linear model under limitations that have more correc- tion and accuracy also failure cases from system instabili- ty. The failure cases may not occur in linear modelling but it was found in non-linear. From these reasons, it becomes the motivation of this study. Both linear and non-linear model of gyro-stabilizer and ship were concerned. Non-linear system modelling of gyro-stabilizer was comprised of ship rolling model and single axis gimbal gyroscope model (non-linear equation of motion). Nor- mally, non-linearity of gyro-stabilizer model appears in restoring term and exciting moment term. However non-linearity of ship roll model able to appear all terms of equation of motion (term of inertia, damping, restoring and exciting moment). In order to formulate non-linear ship roll model, the system identification method is used to find non-linear coefficients of added inertia, damping and restoring term of ship. The system identification methods of ship roll motion can be found in many papers such as Masri et al. (1993) [24] , Chassiakos and Marsi (1996) [25] , Liang et al. (1997) [26] , Liang et al. (2001) [27] , Jang et al. (2009) [28] , Jang et al. (2010)[29] , Jang (2011) [30] and Jang et al. (2011) [31] etc. However, in this paper uses method of Pongdung et al. [32] which is the novel method and able to find all of non-linear terms of ship model. The objective of this presentation is to analyse non-lin- earity of ship roll twin gyro-stabilizer control system under limitations of wave load and precession angle via frequency domain analysis and time domain simulation. 2. Principals and Theories Analysis of gyro-stabilizer system has three parts that is realised. It comprised of water wave model, ship
- 14. 10 Sustainable Marine Structures| Volume 03 | Issue 01 | January 2021 Distributed under creative commons license 4.0 model and gyro-stabilizer model. In order to reach the present objective, the regular and irregular deep water wave models are selected to set simulation cases. The ship and gyro-stabilizer models is concerned both linear and non-linear model to observe and analyze effects of system non-linearity from simulation results. The general principle of gyroscopic stabilization, its torque is produced by gyro-stabilizer that installed in a ship opposes roll exciting moment from water wave. This exciting moment disturbs the angular momentum of fly- wheel such that develops precession motion. The cross product of flywheel angular momentum and precession rate induces moment to resist the exciting moment in op- posite direction [33] . Figure 1 explains working principle of gyro-stabilizer that is installed in a marine vessel. At pres- ent, twin-flywheels are selected, and there are spinning and precession angle rotate in opposite direction. Its result cancels the side effect of gyroscopic moments in the other directions (normally in pitch and yaw of ship). Figure 2 displays the working of twin gyro-stabilizer. Figure 1. Illustration single gyro-stabilizer installation and its working principle Figure 2. Demonstration working principle of twin gy- ro-stabilizer and elimination of its side effect of gyro-sta- bilisation moment 2.1 Full Non-linear Gyro-stabilizer Model The prediction of gyro-stabilizer performances was modelled via the two equations of motions. The first equa- tion is the ship model and the second equation is gyro-sta- bilizer model. Consideration a ship motion, while it is excited by wa- ter wave that is demonstrated in Figure 3. Instantaneous, the ship is rolled by moment of inertia in counter clock- wise and is acted by wave, which wave free surface has β (wave slope angle) against horizontal line. The y axis of body fix frame has φ (roll angle) against horizontal line. Thus θ is relative angle between roll and wave slope angle. The following Newton’s second law, the equation of motion of ship can be written as ( ( )) ( ) ( ) 0 I I θ θ B θ θ C θ θ 44 44 44 44 + + + = a ¨ ¨ (1) where 44 I is moment of inertia of ship that is a constant value. 44 ( ) a I θ is non-linear added moment of inertia function. 44 ( ) B θ is non-linear damping moment function and 44 ( ) C θ is non-linear restoring moment function. In order to find non-linear functions, non-parametric system identification method is used. Briefly, Pongdung’s method [32] is chosen to determine non-linear functions because it able to find all non-linear functions in Equation 1 synchronously. The method needs measured motion data from free roll decay experiment or CFD (Computational fluid dynamics) to formulate inverse problem. Actually, the responses are outputs and are calcu- lated via equation of motion that the non-linear functions of each term are known variable values. On the other hand, the responses become to input (measured data) in inverse prob- lem and the non-linear functions become to output (unknown variables). Each moment terms are solved by inverse prob- lem formalism and stabilized by Landweber’s regularization method. Its solutions are chosen the optimal solution through L-curve criterion. Finally, the zero-crossing detection tech- nique of measured data is compared with that solution for identifying each moment function and reconstruction them. For more detail can see in Pongdung et al. [32] . Figure 3. Rolling of ship on water wave free surface When all non-linear functions is known, substituting DOI: http://dx.doi.org/10.36956/sms.v3i1.316
- 15. 11 Sustainable Marine Structures| Volume 03 | Issue 01 | January 2021 Distributed under creative commons license 4.0 θ φ β = − into Equation 1, it becomes [ ( )]( ) [ ( )]( ) + φ − φ − = I I β β B β β [ ( )]( ) 0 44 44 44 C β β 44 + φ− φ− + φ − φ − a ¨ ¨ ¨ ¨ (2) Rearranging Equation 2, it yields + φ − φ = + φ− + φ − + φ − I I β B β [ ( )] [ ( )] 44 44 44 B β β C β β C β I I β β 44 44 44 44 44 + φ− φ+ φ − φ ( a ¨ ¨ ¨ ) [ ( )] ( a ¨ ¨ ¨ ) (3) Equation 3 is the full nonlinear ship motion: left-hand side is ship moment and right-hand side is exciting mo- ment. The Equation 3 has the same coefficient function in the same term of inertia, damping and restoring: the roll angle and wave slope are equal values at steady state on time domain, but at transient are not. Thus, define τ I I θ β B θ β C θ β w a = + + + [ ( )] [ ( )] [ ( )] 44 44 44 44 ¨ ¨ (4) The wave slope β is determined from Equation 5, which it is regular linear wave (deep water wave). η x t η kx ωt ( , sin ) = − 0 ( ) (5) where 0 η is wave amplitude, k is wave number, x is distance in x direction,ω is angular frequency and t is time. When differentiate Equation 4 with x , it becomes to wave slope equation follow as Equation 6. Then wave slope velocity and acceleration are Equation 7 and 8 re- spectively. ( ) 0 , cos( ) d x t k t dx η β η ω = = − (6) 0 sin( ) k t β η ω ω = − (7) 2 0 cos( ) k t β η ω ω = − − (8) Thus, a model for motion of the ship in roll together with and n -spinning-wheel gyro-stabilizer can be ex- pressed follows as block diagram in Figure 4. Then it can be formulated in equation of motion follow as Equation 9 and 10. I I θ B θ C θ τ τ 44 44 44 44 + φ+ φ + φ = − a w g ( ) ( ) ( ) ¨ ¨ (9) I α B α C α τ τ g g g s p ¨ + + = − sin (10) where α ,α and α in Equation 9 are precession angle, precession rate and precession acceleration respectively. The following Equation 10 g I , and g C are moment of inertia, damping and restoring coefficient of gyro-stabiliz- er about precession axis. Equation 9 represents the full non-linear ship roll dy- namics, while Equation 10 represents the non-linear dy- namics of gyro-stabilizer about the precession axis. The following Equation 9 and Equation 10 associate coupled system, the wave-induce roll moment ( w τ ) excite the ship rolling. When roll motion develops, the roll rate induces a moment about the precession axis of spinning wheels ( s τ ). And then the spinning wheels develop precession, its re- action moment resists on the ship with opposes direction of the wave-induce moment ( g τ ). τ K α s g = φ cos (11) τ nK α α g g = cos (12) where g K is spinning angular momentum ( g spin spin K I ω = ) The roll stabilization moment for passive system can be modified the precession damping and stiffness as well as leave the gyro to free work. For an active system, it is controlled through the precession dynamics via the precession control moment that is PD controller and ex- pressed follow p p d K K τ α α = + (13) where p K is proportional control gain and d K is deriv- ative control gain. The advantage of this control law is no needing ship roll sensors. Figure 4. Block diagram of full non-linear twin gyro-sta- bilizer model DOI: http://dx.doi.org/10.36956/sms.v3i1.316
- 16. 12 Sustainable Marine Structures| Volume 03 | Issue 01 | January 2021 Distributed under creative commons license 4.0 2.2 Linear Gyro-stabilizer Model In the past, analysis any control systems via equation of motions were treated to be linear differential equation. There are reduced complexity and able to transform to s-domain, and then change s-domain to be frequency do- main. At steady state, the analysis control systems through frequency domain are proper. This section uses almost equation from Perez and Steinmann (2009) [23] From Equation 9 and Equation 10, the models are lin- earized: for small angle of roll and precession, the coef- ficients of left-hand side are constant value. However, let define, φ ≈ φ cosα (14) α α α cos ≈ (15) and sin α α ≈ (16) Hence, the linear equation expressed as follow: (I I B C τ τ 44 44 44 44 + φ+ φ + φ = − a w g ) ¨ (17) I α B α C α τ τ g g g s p ¨ + + = − (18) τ I I β B β C β w a = + + + ( 44 44 44 44 ) ¨ (19) τ K s g = φ (20) τ nK α g g = (21) Note that, p τ no changes in linear model. And linear gyro-stabilizer system is demonstrated in Figure 5. Figure 5. Block diagram linear twin gyro-stabilizer model From zero condition, thus the open loop transfer func- tion is H s ol ( ) = = φ τ s ol w ( (s ) ) ( ) I I s B s C 44 44 44 44 + + + a 2 1 (22) The close loop transfer function is (23) where ( ) ol s φ and ( ) cl s φ are Laplace transforms open and close loop roll angle respectively. And the transfer function of precession angle to roll angle is H s cl ( ) = = φ τ s cl w ( (s ) ) (I s B s C I s B s C nK s 44 44 44 2 2 ' ' 2 2 + + + + + I s B s C g g g 2 ' ' )( + + g g g g ) H s pr ( ) = = = α s α s φ( ( s) ) φ ( ( s) ) I s B s C g g g 2 ' ' + + K s g (24) where B B K g g d ' = + (25) C C K g g p ' = + (26) The transfer function of precession angle to wave ex- citing roll moment that is the result from Equation 23 and 24 is = H s H s H s ( pw pr cl I s B s C I s B s C nK s 44 44 44 ( ) 2 2 ' ' 2 2 = = + + + + + τ s α s w ( ( ) ) )( K s ( g g g g g ) ( ) ) (27) Rearranging Equation 24, it yields H s pr ( ) = K Ig g s2 + + B C I I g g s g g ' ' (28) and the roots are p1,2 = − ± − B B C I I I g g g g g g ' ' ' 2 4 (29) Both roots are negative real roots (stability condition) if and only if, 0 g B′ , 0 g C′ also constraint of DOI: http://dx.doi.org/10.36956/sms.v3i1.316
- 17. 13 Sustainable Marine Structures| Volume 03 | Issue 01 | January 2021 Distributed under creative commons license 4.0 B C I I g g g g ' ' 2 4 (30) The following Equation 13, the proportional term is set during design due to centre of mass location, normally locate below the precession axis, which like a pendulum, the g C′ value is fixed. Thus, the control moment becomes τ K α p d = (31) From Equation 30, it able to formulate the condition of two poles to be real root: B C I g g g ' ' 4 (32) It can set as B γ C I r g g g ' ' = 4 , 1 (33) Then substitute Equation 33 in to Equation 25, it be- come K γ C I B r d g g g = − 4 , 1 ' (34) This derivative control gain is used through under con- strained performance follow section 2.4 2.3 Control Performance and Limitations In order to observe the objective performance, the out- put sensitivity function is defined as: S s ( ) φ φ ol cl ( ( s s ) ) (35) The Bode’s integral constraint is ∫ ∞ 0 log 0 S jω dω ( ) = (36) and the roll reduction (complementary sensitivity func- tion) is defined as RR ω S jω ( ) = − = 1 ( ) φ − φ ol cl ( jω jω φcl ) ( jω) ( ) (37) the integral constrain becomes ∫ ∞ 0 log 1 0 − = RR jω dω ( ) (38) Another form of roll reduction function is RR ω ( ) = − 1 H jω H jω ol cl ( ( ) ) (39) Note that, the roll reduction values possible to be neg- ative or positive values but less than 1 along interested range of frequency. The meaning of negative value is roll amplification. The meaning of positive value is roll reduction. Then the roll reduction close to 1, it has better performance. 2.4 Constrained Performance The additional constrain is cause by the precession angle limiting due to mechanical design. If the precession angle reaches this limit, the device may get damage or de- teriorate. Additionally, it may causes of roll amplification rather than roll reduction of ship: phase of resisting mo- ment cannot eliminate wave induce roll moment. For a regular wave of frequency 0 ω and wave height 0 s H , it induces roll exciting moment amplitude 0 w τ . The following Equation 27, it can be obtained the amplitude of precession angle: a 0 = H jω τ pw ( 0 ) 0 w (40) The given constrain is the maximum precession angle max α , it can be obtained the optimal of g B′ that it take the precession angle close to its limit: ( ) ( ) ( ) ( ) * 0 0 0 max 0 argmin , pw g w H j B γ γ ω α ω γ τ ′ = − (41) 3. Cases Study Configurations and Numerical Experiment Setup To carry out the aim of this presentation, the non-linear gyro-stabilizer system is validated with linear gyro-stabi- lizer system to observe and analyze its performance at the same environment and limitation. The vessel was set to zero speed and moved against beam sea direction. In order to determine the system design point, assume that the sys- tem was designed for deep water wave, significant wave height of 0.04 m, all frequency of its. In order to observe and analyze non-linearity of a gyro-stabilizer system, this present, V-hull section was selected because it is a general section profile of high- speed boat and small ship that are suited. The dimensions of selected V-hull are demonstrated in Figure 6. Both linear and non-linear equation of motion was found via DOI: http://dx.doi.org/10.36956/sms.v3i1.316
- 18. 14 Sustainable Marine Structures| Volume 03 | Issue 01 | January 2021 Distributed under creative commons license 4.0 measured data that were the result from CFD method, XFLOW commercial program. The simulations are set to be unsteady flow. There is free roll decay method, which was set initial condition of roll angles are 5, 10, 15, 20, 25 and 30 degrees. According to CFD simulation results, the example of measured data from simulation case, which was set 30 degree of initial condition was demonstrated in Figure 7. Figure 6. The V-hull Geometry and dimension for simula- tion Figure 7. Measured Data from CFD of Initial Condition 30 Degree The linear ship model was formulated from logarith- mic decrement method. The method appropriates to small roll motion less than 8 degree. It requires only roll angle data (measured data from CFD) to detect maxima or min- ima values, where are used to find estimated exponential function. The function leads to determine the damping ratio and natural frequency. There can be converted to add moment of inertia, damping coefficient. However, restor- ing coefficient can be found from inclination calculation. According to this method, the details of calculation were omitted in this presentation. For this paper, the simulation of 5 degree of initial con- dition was used. The estimating of exponential function was shown on Figure 8. According to estimated exponen- tial function the linear ship model is 0.3255 0.0494 19.7321 0 φ+ φ + φ = ¨ (42) Note that, in free roll motion test θ φ = , there are not have relative motion between free surface and roll motion angle and 44 0.26 I = . Figure 8. Determination of exponential function from roll angle data of 5 degree initial condition The non-linear ship model was formulated by re- construction non-linear damping coefficient and added moment of inertia via systems identification method that used all of measured data (roll angle, angular velocity and angular acceleration) and all initial condition from CFD method. The non-linear restoring coefficient function was known via inclination calculation. The calculation result was fitted by polynomial function follow as Equation 43 and its curve was shown at the top of Figure 9. C + φ − φ + φ − φ + φ − φ + 344.3239 283.2101 148.2937 49.4689 10.4220 1.4402 0.1454 44 (φ =− φ + φ − φ ) 6 4 2 19.7321 108.8680 258.2096 12 10 8 18 16 14 (43) The non-linear damping coefficient function was found by accumulating the damping moment data point from system identification method, and there were taken by curve fitting method which is shown in the middle of Fig- ure 9. Then the moment function was divided by φ , thus the non-linear damping coefficient is B44 (φ = φ + ) 0.1588 0.1391 2 (44) From the same procedure of formulating non-linear damping moment, the added moment of inertia fitting curve is shown at the bottom of Figure 9 and its average DOI: http://dx.doi.org/10.36956/sms.v3i1.316
- 19. 15 Sustainable Marine Structures| Volume 03 | Issue 01 | January 2021 Distributed under creative commons license 4.0 value is I44a φ = ¨ 0.066 (45) The full non-linear ship model was simulated on free decay motion with 30 degree initial condition to verify its accuracy. The simulated result of roll angle via Equation 1 was plotted in Figure 10 also result from linear model and measured roll angle from CFD. It proves that the non-lin- ear model is better accuracy more than linear model, when both results were compared with measured roll angle from CFD. Hence, the non-linear ship model has enough accu- racy and can be used in this presentation. Figure 9. The Estimating functions of restoring moment (top), damping moment (middle) and added moment of inertia (bottom) Figure 10. The comparison of simulations between results of non-linear ship model (system identification method) and linear ship model (logarithmic decrement method) with measured data from CFD The non-linear and liner gyro-stabilizer model was used according to Equation10 and Equation 18 respec- tively. There have same characteristic coefficients. The gyro-stabilizer was assumed that its speed is constant at 300 rpm ( spin ω ), the moment of inertia about spin axis is 0.0125 kg-m2 ( spin I ) and the moment of inertia about precession axis is 0.0005 kg-m2 ( g I ). The damping coef- ficient is zero 0 g B = , there is on friction about precession axis. The restoring coefficient is zero; the center of gravity position was located at middle of spin and precession axis (no effect of pendulum). The following Equation 13, the proportional gain p K is fixed value of 0.1. The derivative gain was determined via Equation 34 and Equation 41. Both linear and non-linear systems were determined through frequency domain and simulated with regular waves, which the range of frequencies is 0 to 14.84 rad/ s. The simulation cases of linear system were varied wave amplitudes from 0 η = 0.02 m up to 0 1.5η . The linear sim- ulation case of 0 η was set to be reference case. The * γ of each wave amplitude of simulation cases follow Equation 41 were gathered, and then they were used in simulation cases of full non-linear system. The full non-linear system was simulated in time domain with only amplitude of 0 η . Moreover, in order to find a design trade-off, the * γ of each wave amplitude of linear system were used. The name of simulation cases are described in Table 1. Table 1. The description of simulation cases Case Name Description Gyro-model Ship-model LSOL Linear open loop system - Linear LSCL Linear close loop system Linear Linear LSCL @ 0 cη Linear close loop system that de- termined with wave amplitudes of 0 cη ( c =1.1, 1.2, 1.3, 1.4 and 1.5) Linear Linear FNSOL Full non-linear open loop system - Non-linear FNSCL Full non-linear close loop system Non-linear Non-linear FNS- CL@ 0 cη Full non-linear close loop system with the * γ values of each wave amplitude of LSOL ( c =1.1, 1.2, 1.3, 1.4 and 1.5 ) Non-linear Non-linear 4. Results and Discussions In order to examine the characteristics of performances of non-linear gyro-stabilizer system, the linear system was determined first. The following Figure 11 – 14, they show the results of linear gyro-stabilizer characteristics. Figure 11 show exciting moment (input), the result from Equation 19. The graph was also plotted Stokes lim- it; the exciting moment cannot exceed this line at upper DOI: http://dx.doi.org/10.36956/sms.v3i1.316
- 20. 16 Sustainable Marine Structures| Volume 03 | Issue 01 | January 2021 Distributed under creative commons license 4.0 side. Stokes and exciting moment line of amplitude 0.02 m intersect at 14.84 rad/s, which it was set to be maxi- mum value of frequency range. All lines have tough at ω = 7.786 rad/s that is natural frequency of linear vessel model because it has the same coefficients. And then, when frequencies increase, the exciting moment rapidly increase. It is obviously the increasing of wave amplitudes correspond to increasing exciting moment. Figure 12 and 13 are they were * γ values and preces- sion angles of each wave amplitude respectively. They simultaneously determined via Equation 27 and 41. Figure 12 shows the minimum * γ values of each wave amplitude. All wave amplitudes gave * γ values of 1 until the system gets the exciting frequency that makes the pre- cession angle reach its limit value (60 degree, also observe Figure 13). It has been called critical point. From this point * γ value rapidly increasing to keep precession angle does not exceed 60 degree. As the higher wave amplitude, the critical points have appeared at lower frequency. Figure 13 shows the precession angles in frequency do- main. The trend of precession angle resembles the pose of exciting moment in Figure 11. However, the crests of all lines about exciting frequency value of 2 were affected from turning proportional gain p K that was fixed value of 0.1. And the higher wave amplitude gives the higher precession angle. The flat band at high exciting frequency that has con- stant value of 60 degree is explained as above paragraph. Figure 14 shows the roll angle responses. At the lower exciting frequency from critical point, the result trends resemble precession angle but it rapidly increases when the exciting frequency increase from critical point. The increasing rapidly of roll angle from the critical point is caused by the precession angle reach its limit, which it cannot have more precession rate to create resisting mo- ment for cancel exciting moment. Moreover, at the higher wave amplitude gives the higher roll angle response. Figure 11. Exciting moment of the linear gyro-stabilizer model Figure 12. Estimating of * γ values each wave amplitude from Equation 41 Figure 13. Precession angles of linear gyro-stabilizer model so that determined via Equation 27 and 41 with the following * γ values each wave amplitude in Figure 12 Figure 14. Roll angle responses of linear gyro-stabilizer model each wave amplitude via Equation 23 The following linear model results consistent and rea- sonable to each other. They can be used to compare with the non-linear system in the same wave condition. How- DOI: http://dx.doi.org/10.36956/sms.v3i1.316
- 21. 17 Sustainable Marine Structures| Volume 03 | Issue 01 | January 2021 Distributed under creative commons license 4.0 ever, according to section 3, the non-linear gyro-stabilizer system model was assumed as it was designed to operate in beam sea that it has significant wave height ( 1 3 H ) of 0.04 m ( 0 η = 0.02 m). But it cannot directly determine * γ through transfer function like linear system model. Thus the * γ values each wave amplitude from linear model were applied to non-linear model. The non-linear system needs to simulate in time domain and collected the ampli- tude simulation results at steady state. Hence, Figure 15 - 18 illustrate the comparison between linear and non-linear model. Some data of non-linear model disappear because of its solutions became unstable (no steady state) when the precession angle reached the limit angle. Figure 15 shows the exciting moments both open and close loop of linear and non-linear system. They have the trend like exciting moments of linear system. Linear open loop and close loop exciting moments are same line be- cause they have same coefficients of vessel model. The all non-linear exciting moment lines are above liner exciting moment at high frequencies from around the natural fre- quency. The difference of exciting moment values causes of the coefficient functions follow as Equation 4 that relat- ed to θ , θ and θ . Figure 16 shows the precession angle result that all non-linear precession angles have the same line until they reach to a limited angle at the exciting frequency value of 11.85 rad/s. And almost values are above linear system. The cause of all non-linear precession angles has the same values. They used same * γ =1 as following Figure 12. After they reach the limited angle at higher frequencies, the data lost because the system became unstable so that it now has steady state. However, the non-linear system back to stable at the end of lines be- cause of the increasing of * γ value (see Figure 12) able to reduce the precession angles below limited angle. And then, they obviously show the higher * γ each wave amplitude gave lower precession angle at the higher fre- quencies. The following Figure 17, roll angle responses of the open and closed-loop of linear and non-linear systems are shown. The comparison between open-loop of lin- ear and non-linear system obviously difference behavior along frequencies range; the linear open-loop system is fair curve, while the non-linear closed-loop system has the apex at the frequency value of 6 rad/s. This is the important cause to deep studying throughout non-linear gyro-stabilizer system; a difference response behavior will give the difference a system design point. The linear and non-linear closed-loop systems have a same trend. All non-linear roll angle responses have same values until they reach the frequency of 11.85 rad/s and almost roll angle responses values are above the linear line. The closed-loop non-linear system uses more precession angle than linear system but they give more roll angle responses than linear system. After the critical point of linear closed loop system, all lines of roll angle responses increase and approach to open-loop systems because precession angles were forced and reduced to constant in linear model and non-linear model respectively. This reason refers to ineffi- ciency of the systems, when the precession angle reaches the limited angle. Figure 18 explains the responses in view of a relative roll angle response and can say that are the inverse behav- ior of a roll angle response. While the stabilizer attempt to keep roll angle approach to zero, the difference between a wave slope and roll angle increase (see relation in Figure 3). Figure 15. Comparison exciting moments between non-linear and linear gyro-stabilizer models; the linear model of 0 η was set to be the reference Figure 16. Comparison precession angle responses be- tween non-linear and linear gyro-stabilizer models; the linear model of 0 η was set to be the reference DOI: http://dx.doi.org/10.36956/sms.v3i1.316
- 22. 18 Sustainable Marine Structures| Volume 03 | Issue 01 | January 2021 Distributed under creative commons license 4.0 Figure 17. Comparison roll angle responses between non-linear and linear gyro-stabilizer models; the linear model of 0 η was set to be the reference Figure 18. Comparison relative roll angle responses between non-linear and linear gyro-stabilizer models; the linear model of wave amplitude 0 η was set to be the reference Figure 19. Precession angle responses of linear and non-linear gyro-stabilizer model of wave amplitude 0 η and fix γ =10.6 The non-linear behaviors were analysed and explained throughout frequency domain as they were mentioned above. However, in reality, a * γ value cannot adjust base on frequency domain as following Figure 12; a frequen- cy cannot know immediately in time domain. Thus a * γ will be selected only one value from Figure 12 so that appropriate with operation requirements. The * γ value of 10.6 was selected. It locates at the right end of line LSCL @ 0 η . The value is the highest value, which forces the precession angle to work do not exceed the limited angle throughout the frequency range. Then the selected value was used for both linear and non-linear gyro-stabilizer system. The results were plotted follow as Figure 19 - 22 in frequency domain. In order to examine the effect of non-linearity, linear and non-linear gyro-stabilizer system model were simulated in irregular wave model of Bret- schneider’s [34] method that it has significant wave height of 0.04 m and the results were plot in Figure 23. The following Figure 19, the precession angles of non-linear system work under the limited angle through- out frequency range. It has same characteristic of linear system but has different values. Hence, the roll angle re- sponse in Figure 20 is consequence of precession angle. The roll angle response of non-linear system has same characteristic of linear system but has different values as well. Figure 21 shows the frequency response results. The peak of frequency response of non-linear open-loop sys- tem that refers to the natural frequency of vessel is dif- ferent from linear open-loop system. The curve of linear closed-loop system is fair curve and approach to zero throughout the frequency range. The curve of non-linear closed-loop system has same behaviour of open-loop and its peak is lower. According to Figure 22, the reduction rates of linear and non-linear system are shown. The re- duction rate refers to the efficiency of the stabilizer sys- tem. The non-linear system has the reduction rate values near linear system at the frequencies lower than 6 rad/s. But at the higher frequencies value, the reduction rate val- ues of non-linear system are lower. According to the previous results, the * γ value was selected and fixed. They are made to clearly understand and proved that the non-linearity of non-linear model give a lot of different results at all. Thus, the designing base on linear model may give the wrong respond in reality. In order to prove again, the linear and non-linear gyro-sta- bilizer system models were simulated in same irregular beam sea on time domain. Assume that, precession angle is limited by mechanism at 90 degree but the * γ value was selected from the limited angle at 60 degree of regular wave amplitude in order to let it has margin to prevent a DOI: http://dx.doi.org/10.36956/sms.v3i1.316
- 23. 19 Sustainable Marine Structures| Volume 03 | Issue 01 | January 2021 Distributed under creative commons license 4.0 damage when the gyro-stabilizer system get higher ampli- tude in irregular wave. Figure 20. Roll angle responses of open and close loop condition of linear and non-linear gyro-stabilizer model of wave amplitude 0 η that the close loop model was set the fix value of γ =10.6 Figure 21. Comparison of frequency responses of open and close loop condition of linear and non-linear gyro-sta- bilizer model of wave amplitude 0 η that the close loop models were set the fix value of γ =10.6 Figure 22. Comparison of reduction rate between linear and non-linear gyro-stabilizer model of wave amplitude 0 η that the close loop models were set the fix value of γ =10.6 In the following Figure 23, the setting of gyro- stabilizer simulation cases (linear and non-linear system) were simulated in irregular wave model. The gyro-stabilizers were switched off first, and then be- gin to switched on at 20 second of simulation time to observe the difference of behaviors when the gyro-sta- bilizer models were switch off and on. The systems performances were gathered and shown in Table 2 in root mean square. At the top of Figure 23, the exciting moments of linear and non-linear system were shown. They were induced from the same irregular wave mod- el but it gave exciting moment amplitude and phase shift slightly difference. The RMS of exciting moment of linear model has the value less than non-linear model so that accord to Figure 15. When the stabilizer switched off, the vessel did not stabilize. The roll angle response of linear model shown at the middle of Fig- ure 23 has the RMS value more than non-linear model slightly. These results accord to Figure 20. When the gyro-stabilizers were switched on, the precession began to move for stabilize the vessel, the roll angle respons- es were reduced. The precession angle responses were shown at the bottom of Figure 23. As the gyro-stabi- lizer switched on, the precession angle of linear model has RMS value more than non-linear accord to Figure 19. And the roll angle response of linear model has RMS value lower than non-linear model. However, the reduction rate (RR) of linear system has RMS value more than non-linear system and so very different. As the mentioned these results, they clearly show that the designing via the linear model cloud makes the gyro-stabilizer system miss the design point of mission requirements. On the other hand, the non-linear system model cloud gives more approach to reality for designing follow mission requirements. Table 2. The root mean square values (RMS) of gyro-sta- bilizer system performances in irregular beam sea accord- ing to Figure 23; the significant wave height and average wave frequency is 0.04 m and 10.68 rad/s respectively. Model w τ [Nm] α [deg] φ [deg] RR [-] LSOL 14.5 - 21.56 - FNLSOL 15.38 - 18.57 - LSCL 14.5 32.03 4.75 0.78 FNLSCL 15.05 31.13 11.46 0.38 DOI: http://dx.doi.org/10.36956/sms.v3i1.316
- 24. 20 Sustainable Marine Structures| Volume 03 | Issue 01 | January 2021 Distributed under creative commons license 4.0 Figure 23. Comparision of linear and non-linear gyrosta- bilizer system model when they are swicthed off and on in irregular wave with γ =10. 5. Conclusions A gyro-stabilizer is the interesting system that it can apply to marine vessels for diminishes roll motion. Today it has potentially light weight with no hydrodynamics drag and effective at zero forward speed. The twin-gyroscope was chosen for this presentation because of it not has side effect moment. Almost, the modelling for designing the system use linear model that it might not compre- hensive mission requirement such as high sea condition (high wave amplitude and various frequencies). Thus, the non-linear model becomes important role because it able to give the interested results approach to reality more than linear model. The non-linearity analysis was proved by comparison the results between linear and non-linear mod- el of gyro-stabilizer throughout frequency domain also same wave input, constrains and limitations. Moreover, they were cross-checked by simulating in time domain. Actually, controller gains of the non-linear model can- not directly determine the appropriated turning gain d K via frequency domain analysis. Hence the d K of non-lin- ear model was approximated by selecting the * γ value so that resulted to d K from linear model at the highest frequency value of selected wave amplitude. The compar- ison of interested results as wave exciting moment w τ , precession angle α , roll angle response φ and reduction rate of linear and non-linear close loop model in frequen- cy domain has demonstrated the similar characteristics but gave different values at same frequency obviously. The results were confirmed again by simulation in irregular beam sea on time domain and they demonstrate the differ- ence of behavior of both systems while the gyro-stabilizer was switching on and off. From the resulting analysis, the non-linear gyro-sta- bilizer model gives the results closer to realistic that cor- respond to more accuracy in a designing gyro-stabilizer control system for various amplitudes and frequencies operating condition especially high sea condition. References [1] Chadwick, J.H. (1955), “On the stabilization of roll”, Trans. Soc. Naval Arch. Marine Eng, 63, 237-280. [2] Marzouk, O.A. and Nayfeh, A.H. (2009), “Control of ship roll using passive and active anti roll tanks”, Ocean Engineering, 36(9), 661-671. [3] Haghighi, H. and Jahed-Motlagh, M.R. (2012), Ship roll stabilization via Ssliding mode control and gy- rostabilizer, Bul. inst. politeh. iai Autom. control Comput. sci. sect. J, 1, 51-61. [4] Townsend N.C., Murphy, A.J. and Shenoi, R.A. (2007), “A New Active Gyrostabiliser System for Ride Control of Marine Vehicles”. Ocean Engineer- ing, 34 (11-12), 1607-1617. [5] Lewis, V.E. (1986), “Principles of Naval Architec- ture”. The Society of Naval Architects and Marine Engineers. Chapter IX. [6] Lloyd, A.R.J.M. (1998), “Seakeeping Ship Behaviour in Rough Weather”. Ellis Horwood Ltd. [7] Brennan, L. (1905), Means for Imparting Stability to Unstable Bodies, US Patent 796893. [8] Sperry, E.A. (1908), “Steadying device for vehicles”, Patent US 907907. [9] Schilovski, P.P. (1909), “Gyrocar”, Patent GB 12021. [10] Schilovski, P.P. (1914), “Gyrocar”, Patent GB 12940. [11] Beznos, A.V., Formal’sky, A.M., Gurfinkel, E.V., Jicharev, D.N., Lensky, A.V., Savitsky, K.V. and Tchesalin, L.S. (1998). “Control of autonomous mo- tion of twowheel bicycle with gyroscopic stabiliza- tion”, Proceedings—IEEE International Conference on Robotics and Automation 3, 2670-2675. [12] Karnopp, D. (2002), “Tilt control for gyro-stabilized two-wheeled vehicles”. Vehicle System Dynamics, 37 (2), 145-156. [13] Brown, H.B., Jr. and Xu, Y. (1996), A Single-Wheel, Gyroscopically Stabilized Robot, Proc. of the 1996 IEEE Int. Conf. on Rob. And Autom, Minneapolis, Minnesota. [14] Aubrun, J.N. and Margulies, G. (1979). “Gyrodamp- ers for large scale space structures”, Technical Re- port, NASA CR-159 171. [15] Woolsey, C.A. and Leonard, N.E. (2002), “Stabilizing underwater vehicle motion using internal rotors”, Automatica, 38 (12), 2053-2062. [16] Schultz, C. and Woolsey, C.A. (2003), “An experi- mental platform for validating internal actuator con- trol strategies”, In: IFAC Workshop on Guidance and Control of Underwater Vehicles, April 2003, New- port, South Wales, UK, 209-214. [17] Sperry, E.A. (1910), “The gyroscope for marine pur- DOI: http://dx.doi.org/10.36956/sms.v3i1.316
- 25. 21 Sustainable Marine Structures| Volume 03 | Issue 01 | January 2021 Distributed under creative commons license 4.0 poses”, Transactions of the Society of Naval Archi- tects and Marine Engineers XVIII, 143-154. [18] Schlick, E.O. (1904a), “Device for minimising the oscillatory movements of ships”, Patent US 769493. [19] Schlick, E.O. (1904b). “The gyroscopic effect of fly- wheels on board ship”, Transactions of the Institute of Naval Architects, 23, 117-134. [20] Forbes, T.C. (1904), “Device for steadying ships” Patent US 769693. [21] Sperry, E.A. (1908), “Steadying Device for Vehi- cles”, Patent US 907907. [22] McGookin, M., Anderson, D. and McGookin, E. (2008), “Application of MPC and sliding mode con- trol to IFAC benchmark models”. In: UKACC Inter- national Conference on Control 2008, Manchester, UK. [23] Perez, T. and Steinmann, P. D. “Analysis of Ship Roll Gyrostabilizer Control”, Proceedings of the 8th IFAC International Conference on Manoeuvring and Control of Marine Craft, Guarujá (SP), Brazil. [24] Masri, S.F., Chassiakos, A.G. and Cauchey, T.K . (1993), “Identification of nonlinear dynamic systems using neural networks”, Journal of Applied Mechan- ics. 60(1), 123-133. [25] Chassiakos, A.G. and Masri, SF (1996), “Modeling unknown structural systems through the use of neural network”, Earthquake Engineering Structural Dy- namics, 25(2), 117-128. [26] Liang, Y.C., Zhou, C.G. and Wang, Z.S. (1997), “Identification of restoring forces in non-linear vibra- tion systems based on neural networks”. Journal of Sound and Vibration. 206(1), 103-108. [27] Liang, Y.C., Feng, D.P. and Cooper, J.E. (2001), “Identification of restoring forces in non-linear vibra- tion systems using fuzzy adaptive neural networks”. Journal of Sound and Vibration. 242(1), 47-58. [28] Jang, T.S., Kwon, S.H. and Han, S.L. (2009), “A novel method for non-parametric identification of nonlinear restoring forces in nonlinear vibrations from noisy response data: A conservative system”, Journal of Mechanical Science and Technology. 23(11), 2938-2947. [29] Jang, T.S., Kwon, S.H. and Lee, J.H. (2010) “Recov- ering the functional form of the nonlinear roll damp- ing of ships from a free-roll decay experiment: An inverse formulism”, Ocean Engineering. 37, 14-15. [30] Jang, T.S. (2011), “Non-parametric simultaneous identification of both the nonlinear damping and re- storing characteristics of nonlinear systems whose dampings depend on velocity alone”, Mechanical Systems and Signal Processing. 25(4), 1159-1173. [31] Jang, T.S., Baek, H., Choi, S.H. and Lee S. (2011), “A new method for measuring nonharmonic period- ic excitation forces in nonlinear damped systems”, Mechanical Systems and Signal Processing. 25(6), 2219-2228. [32] Pongduang, S., Chungchoo, C. and Iamraksa, P. (2020), “Nonparametric Identification of Nonlinear Added Mass Moment of Inertia and Damping Mo- ment Characteristics of Large-Amplitude Ship Roll Motion”, Journal of Marine Science and Application, 19(2), 17-27. [33] Arnold, R.N. and Maunder, L. (1961), “Gyrodynam- ics and its Engineering Applications”, Academic Press, New York and London. [34] Bretschneider, C.L. (1959), “Wave variability and wave spectra for win-generated gravity waves”, Technical Memorandum No. 118, Beach Erosion Board, U.S. Army Corps of Engineers, Washington, DC, USA. DOI: http://dx.doi.org/10.36956/sms.v3i1.316
- 26. 22 Sustainable Marine Structures| Volume 03 | Issue 01 | January 2021 Distributed under creative commons license 4.0 DOI: http://dx.doi.org/10.36956/sms.v3i1.378 Sustainable Marine Structures http://ojs.nassg.org/index.php/sms/index ARTICLE Application of Fourth Industrial Revolution Technologies to Marine Aquaculture for Future Food: Imperatives, Challenges and Prospects Sitti Raehanah M. Shaleh1 Rossita Shapawi1 Abentin Estim1 Ching Fui Fui1 Ag. Asri Ag. Ibrahim2 Audrey Daning Tuzan1 Lim Leong Seng1 Chen Cheng Ann1 Alter Jimat2 Burhan Japar3 Saleem Mustafa1* 1. Borneo Marine Research Institute, Universiti Malaysia Sabah, Kota Kinabalu, Sabah, 88400, Malaysia 2. Faculty of Computing and Informatics, Universiti Malaysia Sabah, Kota Kinabalu, Sabah, 88400, Malaysia 3. Korporasi Kemajuan Perikanan dan Nelayan (KO-NELAYAN), Wisma Pertanian Sabah, Kota Kinabalu, Sabah, 88994, Malaysia ARTICLE INFO ABSTRACT Article history Received: 25 May 2021 Accepted: 28 May 2021 Published Online: 10 June 2021 This study was undertaken to examine the options and feasibility of deploy- ing new technologies for transforming the aquaculture sector with the ob- jective of increasing the production efficiency. Selection of technologies to obtain the expected outcome should, obviously, be consistent with the crite- ria of sustainable development. There is a range of technologies being sug- gested for driving change in aquaculture to enhance its contribution to food security. It is necessary to highlight the complexity of issues for systems approach that can shape the course of development of aquaculture so that it can live-up to the expected fish demand by 2030 in addition to the current quantity of 82.1 million tons. Some of the Fourth Industrial Revolution (IR4.0) technologies suggested to achieve this target envisage the use of re- al-time monitoring, integration of a constant stream of data from connected production systems and intelligent automation in controls. This requires ap- plication of mobile devices, internet of things (IoT), smart sensors, artificial intelligence (AI), big data analytics, robotics as well as augmented virtual and mixed reality. AI is receiving more attention due to many reasons. Its use in aquaculture can happen in many ways, for example, in detecting and mitigating stress on the captive fish which is considered critical for the success of aquaculture. While the technology intensification in aquaculture holds a great potential but there are constraints in deploying IR4.0 tools in aquaculture. Possible solutions and practical options, especially with re- spect to future food choices are highlighted in this paper. Keywords: Food security Aquaculture 4.0 Digitalization Imitation seafood Sustainable solutions *Corresponding Author: Saleem Mustafa, Borneo Marine Research Institute, Universiti Malaysia Sabah, Kota Kinabalu, Sabah, 88400, Malaysia; Email: saleem@ums.edu.my 1. Introduction Aquaculture has grown dramatically in the past five decades, with total fish production from this sector amounting to 82.1 million tons [1] . It is widely recog- nized as a potential sustainable solution for food secu- rity and an answer to the need for cost-effective animal protein with low-carbon and ecological footprint. Fish
- 27. 23 Sustainable Marine Structures| Volume 03 | Issue 01 | January 2021 Distributed under creative commons license 4.0 DOI: http://dx.doi.org/10.36956/sms.v3i1.378 demand is projected to increase in the range of 112.1- 114.1 million tons by 2030, requiring an additional 30- 32 million tons of fish [2-4] . This is a great challenge that requires a real transformation. Landings from capture fisheries are unlikely to increase due to depletion of resources and impairment of ocean ecosystem [5] . Aqua- culture production should increase to ensure adequate supplies. Aquaculture has recently surpassed the capture fisheries in production and is expected to share 62% of the food fish by 2030 [6] . The incremental research and developments have been going on and have con- tributed significantly to positioning of aquaculture to where it stands today. However, it is inconceivable to meet this high level of demand without transfor- mation. Because the seafood demand has outweighed production, there is a need for a fast-track approach to producing fish using disruptive innovations and technologies that hold promise for revolutionizing the aquaculture industry. Fourth Industrial Revolution (or IR4.0) offers a range of technologies, and some can be adapted for aquaculture systems. It is appropriate to apply the term ‘Aquaculture 4.0’ to aquatic farming driven by these disruptive technologies. Some interesting work has ap- peared in this area [7-11] . When the sustainability is tak- ing a center stage in food production, it is necessary to generate a strategic framework comprising the purpose or goals and drivers aligned with the United Nations’ Sustainable Development Goals (SDGs) to resonate with the global narrative. Aquaculture 4.0 envisages more automation in se- lected functions, bridging the physical and digital oper- ations through cyber-physical systems using computers for monitoring and algorithmically controlling different mechanisms and processes, and regulating the produc- tion by closed-loop data. Potential areas of application of Aquaculture 4.0 technologies are highlighted in this paper together with the reasons for transformation, chal- lenges and prospects with special focus on future food choices. 2. Technology Intensification Certain new terms such as ‘precision aquaculture’, ‘smart aquaculture’ and ‘digital farming’ are associated with technology intensification in aquaculture. These terms are often used interchangeably but there are sub- tle differences. Precision aquaculture refers to use of digital techniques in monitoring and optimization of the culture system. This implies regulating the supply of inputs in amounts exactly needed to prevent waste, re- duce cost and minimize environmental impacts. Smart farming envisages application of information and data technologies in effective ways for better results. Digital aquaculture combines elements of precision aquacul- ture and smart aquaculture to advance the technology intensification for Big Data analysis, use of web-based platforms and machine learning among other innova- tive tools. Disruptive innovations and technologies of Aquacul- ture 4.0 are believed to hold key to transforming aquacul- ture towards seafood security targets. A comprehensive review of the application of various technological tools, including sensors, robots, 3D prints, drones and artificial intelligence (AI) in aquaculture development has been published recently [12] . Aquaculture industries, and R D organizations are giving increasing attention to embracing modern technologies wherever feasible and affordable. It is being considered as a way of making a major turn- around in aquaculture. Many disrupting solutions are aimed at achieving better output with reduced resource inputs, improved nutritional quality of the harvest, and compliance with the sustainability criteria. This will offer a fast-track approach to meeting the fish production tar- gets through four possible ways: • Greater intelligent automation of the various opera- tions. • Bridging the physical and digital world through cy- ber-physical systems using computers. • Monitoring and algorithmically controlling different mechanisms. • Regulating production steps by closed loop data. Inputs for deployment of modern technologies are in the form of data collections through IoT concept such as wireless network sensors and the analysis of big data with the techniques of AI. For a start, some selective technolo- gies can be tested for their potential outcomes in terms of sustainable production. Aquaculture 4.0 is closely associated with the afore- mentioned technology-intensive aquatic farming. Fore [13] has outlined main steps in modern aquaculture sys- tems (Figure 1) that are aimed at: 1) Improving the ac- curacy, precision and repeatability in the farming pro- cesses, 2) Facilitating a greater degree of automation or inbuilt controls in regular monitoring of the farming system, 3) Providing a dependable decision support system characterized by a computerized program for collecting and analyzing the data, and synthesizing information that can be used to solve the problem by computer algorithms or human intervention or both, and 4) Reducing dependencies on manual labor, and subjective assessments.
- 28. 24 Sustainable Marine Structures| Volume 03 | Issue 01 | January 2021 Distributed under creative commons license 4.0 OBSERVING Sensors Online monitoring INTERPRETATION Predictive models System simulation DECISION MAKING Decision support system Artificial intelligence ACTION Automation Integrated operations Figure 1. Key steps in the production process. The overall objective through all these interventions is to improve productivity, resource efficiency, environmen- tal sustainability and economic benefits. The fish stocked in cultural facilities should be able to perform and express its biological activities. These bio-responses need to be observed (Step-1) and interpreted for assessment of fish condition (Step-2) which will form the basis for making decisions on whether or not any interventions are needed (Step-3), and enforcing actions based on the decision for expected outcomes (Step-4). These steps in the production process can be assisted by appropriate technologies. A model of digital aquaculture system comprising the various tools of technology is presented in Figure 2. Figure 2. Model of a smart aquaculture system. Repro- duced with permission from Tetsuo and Kobayashi [14] . System integration that involves connecting the various aquaculture sub-systems to function as a whole or com- plete unit of operation as can be seen from Figure 2 is the key to success of an intelligent aquaculture [15] . Among the Aquaculture 4.0 technologies, AI is receiv- ing an increasing interest. In a complex system of aquatic food production, AI deployment can happen in many ways. It requires aligning the biological data with comput- ers and understanding how the system works for solving specific problems. Basically, these procedures involve Ar- tificial Neural Network (ANN). ANN is a computational model inspired by neural network of the human nervous system. It mimics the path- ways of brain function where the sense organs perceive stimuli that are transmitted through sensory nerves to the brain that decides how to respond to the stimuli and sends the information through motor nerves to the effector or- gans to act accordingly. This provides the foundation of AI where flow of information happens through algorith- mic pathways. ANN comprises Nodes which in biology are termed as Neurons (or nerve cells) [16] . There are multiple nodes arranged in three layers: Input Layer, Hidden Layer and Output Layer (Figure 3). Figure 3. Three layers of nodes in ANN. • Input layer: Comprises the Input Nodes that bring in- formation from external environment into the network and pass it on to the hidden layer. Information processing and computation are not carried out here. • Hidden Layer: Comprises nodes that have no direct connection with the outside environment (and hence the name ‘hidden’ nodes). Their role is to perform computa- tions and to transfer the information to the output nodes. • Output Layer: Consists of nodes that are responsible for transferring the information to the effector organs which act according to the instructions to produce the end-result or outcome. Nodes from these adjacent layers have connections between them. When an input (stimulus) enters the node, it gets multiplied by a weight value [17] . Weight represents the strength of the signal (or impulse) and determines how much influence the input will have on the output. Weights are the real values associated with the stimulus. Because all inputs (stimuli) are not equal, therefore, weight values are different. DOI: http://dx.doi.org/10.36956/sms.v3i1.378
- 29. 25 Sustainable Marine Structures| Volume 03 | Issue 01 | January 2021 Distributed under creative commons license 4.0 Weights are applied within the nodes of the hidden layer. There is a system in the hidden layer called as an ‘Activation Function’ - that takes the input delivered by input nodes and multiplies it by Weight. Hidden layer also carries out distillation or filtration of inputs (information/ stimuli). It identifies and selects only the important in- formation from the inputs, leaving out the information that is redundant or of no major consequence. If the input (impulse) is relevant and important (higher weight) it is processed for a response. If weights are close to zero, they have lesser importance and stimuli with such weights are filtered out while those of higher weights are retained and processed for response by the fish. There is one more enti- ty called ‘Bias’ (b). Bias is added to the weighted sum and fed through Activation Function as a constant value (=1) which is the Bias Unit. By adding the value ‘1’ the Bias creates a non-zero y-intercept in the linear regression. This enables the ANN to fit the data when all inputs are equal to zero, or in other words, to generate an output when in- put is zero. It allows shifting linearity to either right or left to help the model to fit the given data. Diagrammatically, this pathway can be shown in Figure 4. Figure 4. Response transmission pathway in ANN. Mathematical transformation of this pathway can be expressed by the equation: Input (X) x Weight (W) +Bias (b) Output (Y). Each input (X) is assigned a ‘weight’ (W) value based on its relative importance to all the other inputs. Trans- formation of input into valuable output forms the basis of mathematical constructs where the hidden layer is lo- cated between the input and output of the algorithm, and in which weights are applied to the inputs and directed through an Activation Function as the output. Bias is a mathematical entity that is applied to introduce non-lin- earity in the ANN model. Therefore, Bias is a constant which helps the model in a way that it can fit best in a regression and is an additional parameter in the neural network which is used to adjust the output along with the weighted sum of the inputs. In other words, Bias is an assumption made by a mathematical/ computer model to make the target function easier to process. Bias unit is not connected to or influenced by the input layer activity. It is a fixed value that is added but this role does not amount to any ‘real activity’ (unlike the weight). Thus, the steepness of the sigmoid curve depends on the weight of the inputs, not the Bias. This is evident from the simple formula: Output = sum (inputs x weights) + bias From a biological perspective it is better to explain that the Activation Function is not any structural or anatomical entity but a functional process in the form of mathematical equations that determine the output of a neural network [18] . All input values are close to Zero before weight is applied but once multiplied by weights and summed up they get accumulated, requiring rationalization to keep them in an acceptable range for appropriate output. Activation Func- tion also helps in normalizing the output of each neuron. This envisages reorganization of weighted data and data cleaning by eliminating duplicate and redundant data. Af- ter normalization in the Activation Function, it is possible to generate output that is needed by a fish to respond to conditions it is exposed to. As mentioned earlier, the node (neuron) receives a series of inputs (stimuli) and these are acted upon by Weight and Bias for transformation of input data in the hidden layer, and then the information moves into the next (output) layer of the ANN that tunes it to produce the desired outcome in a specified range for the effectors to react. Complexity of neuronal network requires such a normalization for a rationalized output. Since there are many factors in the cultural environment in which the fish is exposed, the simpler form of the equation: Input (X) x Weight (W) + Bias (B) Output should transform into the following formula: Output, Y = f (X1 x W1 + X2 x W2 + X3 x W3) + b. Neurons of the hidden layer apply the ‘Activation Function’ (f) to the weighted sum of the inputs because of which the fish responds differently according to the nature of stimuli and the needs of the situation. Activation Func- tion provides the type of non-linearity between the inputs and outputs that is biologically logical under a real-world situation. It deserves emphasis that the linear models can- not characterize the nature of relationship between input and output. Without this (Activation Function), the ANN can only generate linear models that do not represent the practical possibilities because most real-world data is non-linear. Response of any aquaculture animal in a cul- tural system or even in the wild is inherently nonlinear in nature. To elaborate it further, it characterizes a situation DOI: http://dx.doi.org/10.36956/sms.v3i1.378
- 30. 26 Sustainable Marine Structures| Volume 03 | Issue 01 | January 2021 Distributed under creative commons license 4.0 where there is no straight-line or direct relationship be- tween an independent variable and a dependent variable. ANN models the input-output that defines the complexity of the perception and processing of the response mecha- nism in the fish. In ANN, the Weight and Bias can be considered as learnable parameters. In biological systems, this amounts to rationalization but in ANN these parameters (Weight and Bias) are randomized and adjusted toward the desired values for the correct output. This is where machine learn- ing is involved. Because the hidden layer has many neu- rons and without activation function there will be a com- bined linear output in response to different inputs. If that is the case, fish response to multiple stimuli will be the same with full energy mobilized into it! Addition of Bias reduces the variance and hence introduces flexibility by addition of a constant value to the sum of Input + Weight. This is done by shifting the result of Activation Function towards the positive or negative side. Weight reflects the strength of the connection. It determines the amount of influence a change in the input will exert on the output. A low Weight value will have no change on the input, and alternatively a higher Weight value will markedly change the output. If Weight value is low, it means that the sen- sation (stimulus) is within the threshold limits of the fish, and it is not necessary for the fish to react. 3. Constraints and Options The main factors constraining technology intensifica- tion are: 1) Reluctance to change, especially in the case of small enterprises, 2) Investment risk that weighs heavily in decision-making in large-scale enterprises, 3) Short- age of skilled human resources, and 4) Lack of gainful employment opportunities for graduates who have the knowledge for high-tech operations. The prospects for aquaculture sector to transform would be brighter with the realization of the inability of the current systems to meet the fish production targets and when significant benefits from technology-intensive systems become apparent. Another reason which will influence market perception will be when the consumers demand to know the methods of growing food and tracer technology gains a foothold. There are three directions for the future growth of aquaculture industry: Optimization of existing cultural systems, genetic modification and new species as future food. 3.1 Optimization of Existing Culture Systems Aquaculture industry has a track record of improv- ing the rearing systems. Knowledge and technological interventions have always supported the production and diversification of aquaculture. As a result, aquaculture has grown at the rate of 7.5 - 9 % annually to emerge as the fastest developing food sector [19,20] . Yet, barring a few selected species of fish, the success in commercial-scale seed production of even the high-value fish remains elu- sive. Most of the seed supplies still come from wild pop- ulations. Captive breeding of bivalve molluscs and sea cucumbers is not always successful. Commercial-scale production of lobster seed in hatcheries remains a chal- lenge. Established aquaculture systems such as those for high-value finfish (groupers, seabass), shrimp and crab have been increasing production to meet the demand in the face of stabilization or decline of capture fisheries using the existing infrastructure. These systems require input of new knowledge for meeting the requirements of sustainable development. 3.2 Genetic Modification Certain genetic modifications have demonstrated a quantum increase in fish growth. Growth rate of some transgenic fish can be increased by 400-600% while si- multaneously reducing the feed input [21] . Genetically engineered tilapia has been reported to put on weight 300 % faster [22] . Genetically modified Atlantic salmon attains market size in half the time (16-18 months) compared to the conventional one that takes 32-36 months [23] . Potential candidate species are certainly those that fetch a high price in the market. In Malaysia and many other countries, the giant grouper (Epinephelus lanceolatus) is highly valued but so far there are no attempts for gene transfer in this species. Nevertheless, such high-value species remain possible candidates. Even with the state- of-the-art farming methods, good care in the hatchery and rich diet, the giant grouper could not gain 1 kg/year. Ob- viously, there are limits to growth. Growth can be modu- lated but it remains within the range set by the genetic and physiological factors. Giant grouper grows to 2.5 meters and attains 400 kg of body weight in about 35- 40 years or even more, gaining about 600 g/year or 1 kg/18 months [24] . Farmed groupers are reared for about for one year and are generally harvested for marketing when 400 - 600 g. Producing genetically modified organisms (GMOs) is a controversial topic. Although market forces see benefits, but conservation biologists are concerned about the eco- system implications of GMOs in case of their entry into the wild. Consumer perception about GMOs is divided between acceptance and rejection. Resolution of this issue will take time. Future food security challenges and climate change DOI: http://dx.doi.org/10.36956/sms.v3i1.378
- 31. 27 Sustainable Marine Structures| Volume 03 | Issue 01 | January 2021 Distributed under creative commons license 4.0 adaptations will probably shape the cost/benefit analysis of genetically modified fish. For now, the aquaculture can remain focused on the non-controversial methods of pro- duction. 3.3 New Species as Future Food While optimization of culture systems of currently used species will continue its transformation trajectory, new species can be tested for potential development as future seafood. Being new candidates, it will be easier to apply modern technologies for starting from scratch for sustain- able production since it will be free from the legacies of the existing infrastructure and practices. As a dynamic sector, aquaculture has been diversifying and adapting over time for better production and sustain- ability. However, now the food security challenges are different and require more efforts for screening new can- didate species and selecting those that possess biological attributes needed for sustainable aquaculture. The criteria for selecting new species should be based on multiple qualities. Currently, out of 33,600 species of finfish re- corded so far, only 608 species have been tried for aqua- culture [3] . The number of invertebrate species is much higher. Of these, there are only 10 top farmed species, and this number includes shellfish as well as seaweeds in addi- tion to finfish. 3.3.1 Criteria for Selection Adaptability to captivity at high-stocking density. High rate of survival and fast growth. Acceptance of artificial diets and nutritional efficien- cy. Ability of physiological systems to accept interven- tions aimed at developing farmed fish as functional food. Resilience to the effects of climate change and other external drivers. Amenability to farming in compliance with sustain- ability requirements. Consumer preference and marketability. 3.3.2 Advantages of Selecting New Species (1) Aquaculture industry can make a fresh start, with- out the need for replacing the existing expensive culture infrastructure. (2) Culture systems for the new species can be de- signed in response to prevailing challenges. (3) Leveraging of past experience. (4) New systems could offer scope for: a) Disruptive innovations. b) Green technology based on circular / decarbonized production models. c) Potential for developing into functional seafood sys- tem through bioencapsulated ration or other means. 4. Tilapia (Oreochromis niloticus) as a Model Tilapia (Oreochromis niloticus) is the subject of an ongoing study. It is a commercially important fish global- ly. Tilapia ranks among the top 10 farmed species in the world that are expected to drive future growth of aqua- culture and is expected to yield 7.3 million tons a year by 2030 [6] . Being one the most thoroughly investigated fin- fish in the world, the data generated on its performance in culture systems offers a great deal of insights needed for AI application. Some basic attributes of tilapia specifically suitable for AI application in areas such as stress detection include: A strong receptor-effector system. Well-defined behavioral changes in response to en- vironmental factors (stressors). Sensory systems having a remarkable ability of per- ceiving external stimuli and provoking reaction in a way that helps the fish adjust to the prevailing conditions. Stress-detecting algorithms can help in timely mitigat- ing stress and even prevention, and in optimizing culture conditions. Reducing stress on captive fish is vitally im- portant for sustainable production [25] . The stress response of the fish has been documented by several authors in the past [26-34] . A simplified response of tilapia to stress factors is shown in Table 1. A real-life response system is complex, depending on many factors. The response is modulated by magnitude of stress factors and their possible interdependence, and duration of expo- sure. This should be considered in algorithm development. Still, such data provide a scientific basis for developing algorithms to detect and mitigate the stress factors for op- timizing the culture conditions. Table 1. Response of tilapia to stress factors. Variable Fish response Temperature, o C Decrease in opercular movement with temperature decline. DO, mg/l Reduced random movement, decline in swimming activity. Increased surfacing, crowding, sluggish response to stimuli. Salinity, ppt Increased opercular rate, increased vertical move- ment, disorientation, exhaustion, more time at bottom. Unionized am- monia (mg/l) Initial shock, increased agitation followed by decline, increased vertical movement, disorientation. The fish is exposed to different stimuli at the same time DOI: http://dx.doi.org/10.36956/sms.v3i1.378
- 32. 28 Sustainable Marine Structures| Volume 03 | Issue 01 | January 2021 Distributed under creative commons license 4.0 in the culture system or wild environment and reacts as appropriate. The stress factors that serve as stimuli may be: Decline in dissolved oxygen Increase in nitrite and ammonia Rise in water temperature Visual factors Auditory factors Others Their perception and transmission are depicted in Fig- ure 5. Figure 5. Architecture of multi-layer perception network for prediction of selected water quality parameters. These stimuli are different in scale and in producing physiological effects in the fish. Their lethal effects are unequal. The stimulus-response, therefore, cannot be a linear process. Thus, in a real-world situation the fish cannot live with the same (or linear) response to factors that are profoundly different. Specific nature of visible response provides the basis of algorithm development. Learning from the fish’s stimuli filtering system for response prioritization and modulating an effective out- come are necessary for mathematical constructs of AI. Just as there are different impulses or stimuli or stress factors, there should be multiple algorithms to perform the calculations on the input data to be able to compute the output (=biological response of fish). A biological ANN model that maps the inputs and outputs is complex and has a high level of dimensionality to factors such as dissolved oxygen deficiency, spike in ammonia concen- tration or change in salinity, thus requiring big data and analytics. An Ongoing study on stress-detecting algorithm for Tilapia aquaculture optimization through artificial intelli- gence intervention consists of following components: (1) Data collection: pH, temperature, salinity, and DO) 10 minutes for 1 month. (2) Summation of the data. (3) ANN design specifications. (4) Network designing. (5) Testing the ANN design by comparing the actual data with the predicated data output. For practical purposes and prompt mitigation action, a real-time monitoring of cultural conditions through sen- sors and the fish’s visible (behavioural) response through visual analytics will be required. Use of sensors will be for water quality measurement, but information of mon- itoring and assessment of fish behaviour and condition will also be very helpful in managing the cultural system. Production efficiency will be high if the fish is in a physi- ologically robust condition that supports faster growth and health. The process would involve consolidation of sen- sor-generated data and computer (digital) vision through interactive interfaces that provide a scientific basis for timely action and data interpretation for Decision Sup- port System (DSS). DSS is a computerized system for collecting and analyzing data, synthesizing the required information to support an efficient decision-making for solving problems in aquaculture systems. It will com- prise integrated components in the form of: Physical objects (“things”) such as sensors, software and other multiple technologies, real-time analytics and machine learning, stocked fish, and transfer of data over a net- work without human-to-human or even human to com- puter interaction. This defines the IoT in a digital aqua- culture system with specific features for: Automation of control systems, real-time monitoring of water quality and stocked fish, reduced dependencies on technical personnel, doing away with subjective assessments and improved production efficiency. 5. Future Food Choices As pointed out earlier, digitalization of existing aqua- culture systems can be gradually increased, but it will be more practically convenient and cost-effective to develop aquaculture 4.0 systems for certain future foods, especial- ly microalgae, seaweed and mushrooms. There is nothing very technical about this popular term ‘Future Food’. Some authors define it as food that we will be eating in 20 years' time, but it is better to refrain from fixing any year or decade as it seems arbitrary. Food secu- rity is a challenge for the world and is likely to get bigger in near future. What we will be eating in the future is a matter that is receiving a great deal of attention (Table 2). When it comes to seafood, we expect to consume: (1) More of the same food that we are consuming now. (2) Same seafood produced in different ways. (3) Same seafood raised by different culture systems (for example. organic farming). (4) New species as functional food. (5) 3D-printed seafood. DOI: http://dx.doi.org/10.36956/sms.v3i1.378
- 33. 29 Sustainable Marine Structures| Volume 03 | Issue 01 | January 2021 Distributed under creative commons license 4.0 Table 2. Trends in future seafood. Known types of foods currently harvested Advantages from future farming Species in the current production systems Species in the future produc- tion systems Fish, shrimp, crab from capture fisheries or aquaculture. Organic fish, shrimp, and crab produced through certified organic aquacul- ture methods. Better for health. Lobster from the wild population. Hatchery-produced and farmed lobster. Increase in supply. Decrease in over- fishing pressure. Fish farmed as primary food. Fish as functional food through bioencapsulated/ fortified diet. Specific health bene- fits. Microalgae harvested from the sea. Commercial-scale pro- duction under controlled conditions. Exploring the poten- tial of new species could open many horizons for their use as functional or regular food for humans. Mushroom (Fungi). Known or new spe- cies raised through agri-aquaculture systems “Mush-aquaculture”. Mushrooms are a source of nutraceuti- cals and therapeutic substances, and bioactive com- pounds of functional significance. Application of AI and other tools of current technol- ogies to future aquatic food is practically more plausible due to a number of reasons: 1) Opportunity to select species that can be grown on a fast track, 2) Determining amenability of new species to technology-intensive farm- ing systems, 3) Shaping the production systems consistent with the sustainability requirements, and 4) Using species according to their: a) ability to accept nutrient fortifica- tion and bioencapsulation, b) qualities that make them acceptable as functional food when modulated, c) bio- logical capacities for accepting integration into a system of macro-cascading for eco-friendly spinoff benefits or socio-economic dividends, and d) suitability for organic aquaculture. These topics have been elaborated in this pa- per. 6. Emerging Area: Imitation Seafood There is a growing interest in imitation seafood. Sev- eral plant-based recipes for imitation fish are being devel- oped, for example, raw tuna and salmon. The 3D printed imitation seafoods are opening a vast horizon of new op- portunities. This is an area with enormous potential for: ‘Secret’ recipes Innovation Entrepreneurship Graduate employment Start-ups. Making plant-based fish alternative will require inter- disciplinary collaboration involving aquaculture, food science and technology, culinary art, psychology and eco- nomics. Interdisciplinary research is making it possible to produce vegetable-based products that taste like a fish. With more efforts it might be possible to process to- mato or other vegetables into a product that resembles the taste of grouper, hump-head wrasse or other spe- cies! This topic can inspire researchers of all ages and backgrounds to push the boundaries of innovation and entrepreneurship. However, momentum for such future foods should develop for the sake of human health, en- vironment, creating new areas for entrepreneurship and life-long learning. It requires changing the mindset. In the words of a pro- tagonist of regenerative agriculture, Charles Massy “To- day’s problems cannot be solved with today’s mind”, and thus, we need to change our mindscapes before we can change our food security landscapes. Promising sources of ingredients for imitation seafood are seaweeds and mushroom among others. Malaysia has a vast coastal marine area for seaweed farming. The growing conditions are favorable. Mushrooms are easy to farm under certain agri-aquaculture systems due to their survival and growing conditions in a tropical climate. They do not carry out photosynthesis and, thus, there is no need for vast sun-exposed areas. Fibrous nature of mush- rooms offers the advantage to turn them into products that in some way resemble that from animal sources. Further- more, mushrooms do not have a strong taste or flavour, and this helps in culinary processing for imitation. Use of mushrooms in such future foods will intensify interest in exploring the most suitable edible species out of the thou- sands that are known to occur. There are good and bad practices that can be associated with the imitation seafood (Table 3). Table 3. Good and bad practices requiring attention. Good practices 1. Environment-friendly. 2. Spare animals from the stress of captivity, cruelty and sacrifice. 3. Free of seafood poisoning risk. 4. Free of zoonotic infection concerns. 5. Easy to manipulate nutritional and organoleptic quality by culi- nary processing. 6. Easy to develop into functional food. 7. Produced according to demand and save the product from loss in case of supply chain disruption. Bad practices 1. Counterfeit food products containing false claims of functional properties or even containing harmful substances. DOI: http://dx.doi.org/10.36956/sms.v3i1.378
- 34. 30 Sustainable Marine Structures| Volume 03 | Issue 01 | January 2021 Distributed under creative commons license 4.0 7. Solutions Quality control offers the best solution to curb malprac- tices in the imitation seafood business. Once the consumer interest picks up, the market is likely to expand and the public will be better informed. The nature of ingredients, quality assurance, price, awareness campaigns and con- sumer appeal will determine their success in the market. Certification procedures, traceability and blockchain will be needed to ensure that only quality product enters the market and consumers get what is claimed by the pro- ducers of the food item on the label. Blockchain has the capability to trace the entire lifecycle of food products from origin to consumers. It can help combat fraud in the food market, boost transparency in operations involved in processing. It will be possible to track and trace any sea- food in the market and might emerge as the solution to the critically important food safety. 8. Conclusions Aquaculture has grown rapidly over the recent decades. With the stagnation in capture fisheries landings, aqua- culture should further enhance its contribution to seafood supply and food security. It has reached a stage that the trend of incremental change can shift towards transforma- tion using IR4.0 technologies. There is an urgent need to examine if the current research and development efforts are following a system / systems / systemic or systematic approach. Investment in future food necessitates a serious introspection for an outcome-based and solution-oriented research that is linked to market demand and societal wel- fare, and is also rooted in environmental sustainability. Acknowledgement This study was supported by Aquaculture Flagship pro- gram of Universiti Malaysia Sabah. References [1] SOFIA. The state of world fisheries and aquaculture. Food and Agriculture Organization, Rome, Italy, 2020. [2] Kobayashi, M., Msangi, S., Batka, M. et al. Fish to 2030: The Role and Opportunity for Aquaculture. Aquaculture Economics and Management, 2015, 19 (3), 282-300. [3] Cai, J. and Leung, P.S. (2017). Short-term projection of global fish demand and supply gaps. Fisheries and Aquaculture Technical Paper no. 607, Food and Ag- riculture Organization, Rome, Italy, 2017. [4] FAO. The state of world fisheries and aquaculture. Food and Agriculture Organization, Rome, Italy, 2018. [5] Mustafa, S. and Saad, S. Coral Triangle: Marine bio- diversity and fisheries sustainability. In: Leal Filho W., Azul A.M., Brandli L., Lange Salvia A., Wall T. (eds) Life Below Water. Encyclopedia of the UN Sustainable Development Goals. Springer, Cham, Switzerland, 2021. [6] WB. Fish farms to produce nearly two-thirds of glob- al food fish supply by 2030. The World Bank, Wash- ington, DC, 2014. [7] Mustafa, F.H., Bagul, A.H.B.P., Senoo, S. and Shapa- wi, R. A review of smart fish farming system. Journal of Aquaculture Engineering and Fisheries Research, 2016, 2 (4), 193 - 200. [8] Vik, J.O. Digi Sal: Towards the digital salmon- from a reactive to a proactive research strategy in aqua- culture. Norway University of Life Sciences, Oslo, Norway, 2016. [9] Lu, H. D., Yu, X., Liu, G. Q. Abnormal behavior detection method of fish school under low dissolved oxygen stress based on image processing and com- pressed sensing. Journal of Zhejiang University (Ag- riculture and Life Ences), 2018, 44(4), 499- 506. [10] Chen, Y. Q., Li, S. F., Liu, H. M., Tao, P. Application of intelligent technology in animal husbandry and aquaculture industry. 14th International Conference on Computer Science Education (ICCSE). IEEE, Toronto, ON, Canada, 2019. [11] Helland, S. How digitalization is refining aquaculture research. The Fish Site. The Fish Site, Hatch Accel- erator Holding Limited, Cork, Ireland, 2020. [12] Mustafa,S., Estim, A., Shapawi, R., et al. Technolog- ical applications and adaptations in aquaculture for progress towards sustainable development and sea- food security. IOP Publishing, Bristol, UK, 2021. [13] Fore, M. Precision fish farming: A new framework to improve aquaculture, Part 1. Global Aquaculture Al- liance. New Hampshire Avenue, Portsmouth, USA, 2019. [14] Tetsuo, I. and Kobayashi, T. Smart aquaculture sys- tem: A remote feeding system with smartphones. Proceedings of the 2019 IEEE 23 International Sym- posium on Consumer Technologies, pages 93-96. DOI: v10.1109/ISCE.2019.8901026. [15] Li, D. and Li, C. Intelligent aquaculture. World Aquaculture Society, Los Angeles, USA, 2021. [16] Ogajanovski, G. Everything you need to know about neural netyworks and back propagation- machine learning easy and fun. Towards Data Science, Media, Canada, 2019. [17] Alammar, J. A visual and interactive guide to the ba- sics of neural networks. Creative Commons Attribu- DOI: http://dx.doi.org/10.36956/sms.v3i1.378
- 35. 31 Sustainable Marine Structures| Volume 03 | Issue 01 | January 2021 Distributed under creative commons license 4.0 tions, 2018. [18] Nizrak, A. Comparison of activation function for deep neural networks. Yildiz Technical University, Istanbul, Turkey. [19] SOFIA. The state of world fisheries and aquaculture Food and Agriculture Organization, Rome, Italy, 2018. [20] IFFO. Aquaculture. IFFO Marine Ingredients Orga- nization, London, UK, 2021. [21] Towers, L. Importance of transgenic fish to global aquaculture- a review. The Fish Site, Hatch Accelera- tor Holding Limited, Cork, Ireland, 2016. [22] Muir, W.M. The threats and benefits of GM fish. EMBO Report, 2004, 5 (7), 654 - 659. [23] Williams, D. Genetically modified salmon to hit US markets. CGTN America, Washington, DC, 2019. [24] Tucker, J.W. Species profile: grouper aquaculture. Southern Regional Aquaculture Center (SRAC), Pub- lication No. 721. Fort Pierce, Florida, USA, Division of Marine Science Harbor Branch Oceanographic Institution, 1999. [25] Aerts, J. Stress in aquaculture: a rough guide. The Fish Site, Hatch Accelerator Holdings, Cork, Ireland, 2019. [26] Fernandes, M.N., Rantin, F.T. Relationships between oxygen availability and metabolic cost of breathing in Nile tilapia (Oreochromis niloticus): aquacultural consequences. Aquaculture, 1994, 27:339-346. [27] Moreira, P.S.A. and Volpato, G.L. Conditioning of stress in Nile tilapia. Journal of Fish Biology 2004, 64, 961-969. [28] Jian-yu, X., Xiang-wen, M., Ying, L. et al. Behav- ioral response of tilapia (Oreochromis niloticus) to acute ammonia stress no0nitored by computer vision. Journal of Zhejiang University. Science, 2005, 812 - 816. [29] Xu, J., Liu, Y., Cui, S. et al. Behavioral responses of tilapia (Oreochromis niloticus) to acute fluctuations in dissolved oxygen levels as monitored by computer vision. Aquaculture Engineering 2006, 35, 207 - 217. [30] Barreto, R.E., Volpato, G.L., Faturi, C.B., et al. Ag- gressive behaviour traits predict physiological stress responses in Nile tilapia (Oreochromis niloticus). Marine and Freshwater Behavior and Physiology, 2009, 42,109-118. [31] Barreto, R.E., Miyai, C.A., Sanches, F.H.C. et al. Blood cues induce antipredator behavior in Nile Tila- pia conspecifics. PLoSOne, 2013, 8: e54642. [32] Hassan, M., Zakariah, M.I., Wahab, W. et al. Histo- pathological and behavioral changes in Oreochromis sp. After exposure to different salinities. Journal of Fisheries cand Livestock Production, 2013, 1, 103. DOI: 10.4172/2332-2608.1000103. [33] King, M. and Sardella, B. The effects of acclimation temperature, salinity, and behavior on the thermal tolerance of Mozambique tilapia (Oreochromis mos- sambicus). Journal of Experimental Zoology Part A Ecological and Integrative Physiology, 2017, 327(7) https://doi.org/10.1002/jez.2113. [34] Panase, P., Saenphet, S. and Saenphet, K. Biochem- ical and physiological responses of Nile tilapia, Oreochromis niloticus Lin subjected to cold shock of water temperature. Aquaculture Reports, 2018, 11, 17-23. DOI: http://dx.doi.org/10.36956/sms.v3i1.378
- 36. 32 Sustainable Marine Structures| Volume 03 | Issue 01 | January 2021 Distributed under creative commons license 4.0 http://dx.doi.org/10.36956/sms.v3i1.414 Sustainable Marine Structures http://ojs.nassg.org/index.php/sms/index ARTICLE A CFD Study of the Resistance Behavior of a Planing Hull in Restricted Waterways Ahmed O. Elaghbash* RSC for engineering and naval architecture and consultancy, Sudan ARTICLE INFO ABSTRACT Article history Received: 25 June 2021 Accepted: 28 June 2021 Published Online:10 July 2021 The demand for high-speed boats that operating near to shoreline is increasing nowadays. Understanding the behavior and attitude of high- speed boats when moving in different waterways is very important for boat designer. This research uses a CFD (Computational Fluid Dynamics) analysis to investigate the shallow water effects on prismatic planing hull. The turbulence flow around the hull was described by Reynolds Navier Stokes equations RANSE using the k-ɛ turbulence model. The free surface was modelled by the volume of fluid (VOF) method. The analysis is steady for all the ranges of speeds except those close to the critical speed range Fh =0.84 to 1.27 due to the propagation of the planing hull solitary waves at this range. In this study, the planing hull lift force, total resistance, and wave pattern for the range of subcritical speeds, critical speeds, and supercritical speeds have been calculated using CFD. The numerical results have been compared with experimental results. The dynamic pressure distribution on the planing hull and its wave pattern at critical speed in shallow water were compared with those in deep water. The numerical results give a good agreement with the experimental results whereas total average error equals 7% for numerical lift force, and 8% for numerical total resistance. The worst effect on the planing hull in shallow channels occurs at the critical speed range, where solitary wave formulates. Keywords: Planing hull High speed craft Shallow water Deep water Solitary wave Critical speed Total resistance CFD Channel Restricted water *Corresponding Author: Ahmed O. Elaghbash, RSC for engineering and naval architecture and consultancy, Sudan Email: men.aom90@gmail.com 1. Introduction In this chapter, try to interpret the operation theory of high speed craft which considers the planing hull as the main part of it. Also, it is displayed an objective, purposes of the research, and thesis structure. 1.1 High-speed Craft Many researchers have tried to define high-speed ves- sels; the first one is Baird (1998) who says the speed of the ship up to 30 knots. Also, some hydrodynamicists say when the Froude number above 0.4 such as high-speed monohulls and catamarans. Recently, definition of high speed craft, it means a craft that is operable on or above the water and has characteristics so different from those of conventional displacement ships, to which the existing international conventions, particularly SOLAS, apply, that alternative measures should be used to achieve an equivalent level of safety. In order to be considered a high speed craft, the craft must be
- 37. 33 Sustainable Marine Structures| Volume 03 | Issue 01 | January 2021 Distributed under creative commons license 4.0 http://dx.doi.org/10.36956/sms.v3i1.414 capable of a maximum speed equal to or exceeding V = 3.7 displ .1667, where V is the maximum speed and displ is the vessel displacement corresponding to the design waterline in cubic meters. The classification of high- speed vessels depends on the forces carrying them; Figure 1 shows these forces which support the vessels. On the left-hand side, show the vessels supported by air, which includes Air Cushion Vehicles (ACV), Surface-Effect Ships (SES) and wing-in-ground craft (WIG). Figure 1. types of high speed craft On the right-hand side, the Vessels are supported by a hydrostatic force which is given from the submerged part of the vessel (buoyancy), called Displacement vessels include conventional monohull, catamaran, trimaran, small water-plane-area twin-hull (SWATH). In between displacement vessels and air-supported, there is a hydrodynamics force supported vessels as a result of vessel forms such as planing hull or foil support including the surface pricing foil (Hydrofoil) and Submerged Foil (jet foil). They all suffer from the common problem of limited payload and sensitivity to wind and sea state. While each type of vessel has its unique characteristics depended to form of the vessel. 1.1.1 Wing-in-ground (WIG) Craft Wing-in-ground (WIG) crafts are supported in their main operational mode solely by aerodynamic forces which enable them to operate at low altitude above the sea surface but out of direct contact with that surface. Accordingly, their arrangement, engineering characteristics, design, construction and operation have a high degree of commonality with those characteristics of aircraft. However, they operate with other waterborne craft and must necessarily utilize the same collision avoidance rules as conventional shipping. WIG craft is a multimodal craft which, in its main operational mode, flies by using ground effect above the water or some other surface, without constant contact with such a surface and supported in the air, mainly, by an aerodynamic lift generated on a wing (wings), hull, or their parts, which are intended to utilize the ground effect action [1] . WIG crafts are categorized according to the following types: • Type A: a craft which is certified for operation only in ground effect. Within prescribed operational limitations, the structure and/or the equipment of such a craft should exclude any technical possibility to exceed the flight altitude over the maximum vertical extent of ground effect. • Type B: a craft which is certified for main operation in ground effect and to temporarily increase its altitude outside ground effect to a limited height, but not exceeding 150 m above the surface, in case of emergency and for overcoming obstacles. • Type C: a craft which is certified for the same operation as type B; and also for limited operation at altitude exceeding 150 m above the surface, in case of emergency and for overcoming obstacles. IMO and the International Civil Aviation Organization (ICAO) have agreed that any WIG craft capable of sustained flight outside the influence of ground effect should also be subject to the rules and regulations of ICAO. Other crafts, including those with limited fly- over capability, should be covered only by the maritime regulatory regime. 1.1.2 An Air Cushion Vehicle (ACV) An Air Cushion Vehicle is a vessel that is completely carried by air pressure, in the near vicinity to the surface. It is suitable for utility over earth or water. Its flexibility makes it a vehicle of choice in circumstances where region remoteness, insufficient water profundity, or need of shoreline facility. To hold the cushion of air under the boat, it is outfitted with an elastic seal called a skirt. Air leakage out from down the skirt compensated by lift fans. The air pressure under the boat should be a balance for load weight on the boat. 1.1.3 Surface Effect Ships (SES) A Surface Effect Ship operates merely above water and a little portion displacement, about 10%, is propped by catamaran-similar side hulls. This sort of vessel features a shallow draft than a routine catamaran and due to the minimal displacement, makes significantly less wake. Cause to the fixed side hulls, a SES has elastic seals only at the stern and bow and demand lower lift capacity than an ACV. 1.1.4 Jet Foils and Hydrofoils Jet foils and hydrofoils have a more profound displac-
- 38. 34 Sustainable Marine Structures| Volume 03 | Issue 01 | January 2021 Distributed under creative commons license 4.0 ement than SES, ACV, and trend to be steadier, giving a smoother traveler ride. Hydrofoils fly on wings in the water: whereas, hovercraft float on a layer of air above the water. Cause of their deeper displacement they are also more susceptible to damage from floating debris. Foils extend from movable arms that act as the lifting surface at operational speeds. The foils make a lift that completely raises the monohull out of the water. Hydrofoil parts can be partitioned into two main classifications, surface- piercing and completely submerged. A hydrofoil must be able to function securely while both hulls are borne mode and while takeoff. 1.1.5 Planing Hull Vessels The vessel lifted by bouncy at Froude number under 0.4 is called a displacement ship. Whereas when carrying most of the weight of the ship by hydrodynamic force at Froude number above to 1 it’s called a planing vessel. While the semi-planing vessel is in the range of Froude number between 0.4 and 1. Planing craft is employed as navy boats, racing boats, service boats, recreational boats, and ambulance boats. In general, the planing hull sailing with trim by stern means rise the bow and immerse the stern as a result of planing surface. Figure 2 shows the hydrodynamic force push the planing hull outer the water to reduce the resistance and increase the speed. Figure 2. hydrodynamic lift of planing hull 1.1.6 Monohull Vessels Adjusted from the army, the high execution monohull design can be seen in corvettes and destroyers. These vessels are distinguished by a slim limit hull, high speed, and the capacity to function in shifted climate conditions. Whereas exceedingly maneuverable, this sort of vessel is delicate to wind and wave activity unless stabilizers are utilized. The fast ship design is an illustration of a high execution monohull design, and Norasia is a case of a monohull with stabilizers. This design offers the highest cargo carrying capacity per ton. Most monohulls vessels work on comparatively lower ranges of speeds. 1.1.7 Catamaran Vessels A catamaran vessel has two displacement hulls. It utilizes when required for a spacious area on the deck and high stability in sailing. This feature is comfortable for a passenger, and suitable for low-density cargoes. For that, it uses in fast ferries. • Asymmetrical Catamarans: The more prevalent basic form of a multihull in the 1970s than nowadays. It is a monohull vessel with a slot cut out within the center. • Symmetrical Catamarans: An amended catamaran design with many hull shape variations that feature two symmetrical hulls. This improves the performance and course keeping of the asymmetrical catamaran. This multihull is designed to move a range of displacement speeds semi-displacement speeds. Always, the level of the main deck is higher than operational waterline leading to improve sailing at rough sea, in comparison with Asymmetrical multihulls. • Wave-Piercing Catamarans: this design populates by INCAT of Australia. It is asymmetrical multihulls with an elongated bow-section. The bow-section designs have many versions, notices hard taper at the end. It is designed to work under the waterline and the after sections operate in a displacement/semi-displacement mode. Since water is more stable on the underwater surface, the wave-piercing catamaran has excellent exposed water performance. 1.1.8 Small Water-plane Area Twin Hull (SWATH) Small water-plane Area Twin Hull is considered an amendment of a catamaran design. Its design is recognized by two tubular immersed in water under hulls, fixed to the upper catamaran frame by slim struts. The torpedo-shaped hulls work beneath the waterline. The concept is to put the ship’s buoyancy under the water surface and give minimal surface region at the waterline for waves to act upon, creating great sea-keeping capacity. 1.2 Objectives of Research This work is shedding light on The CFD capability measurements to capture all phasic and phenomena in different waterways. Furthermore, the shallow channel hydrodynamic forces effect on planing prismatic hull compared with hydrodynamic forces in open water; this was conducted through a transition from displacement speed to planning speed. 1.3 Purposes of Research The following questions are addressed in this thesis. • How accurately can the hydrodynamic forces on a planing hull be predicted using CFD simulations? • Do the CFD simulations yield any additional information that is not obtained from model testing? • What are phenomena which occur in the free surface during moving the planing hull in different waterways for http://dx.doi.org/10.36956/sms.v3i1.414
- 39. 35 Sustainable Marine Structures| Volume 03 | Issue 01 | January 2021 Distributed under creative commons license 4.0 a transition from displacement to planing speed? 1.4 Thesis Structure The thesis is divided into six chapters: Chapter 1 includes the dividing of high-speed vessels depending on type of force which supported vessel, three main forces; Air support, hydrodynamic support, and buoyancy support. Also, it is displayed an objective and purposes of research, and thesis organization. Chapter 2 includes the literature reviews of planning hull analysis techniques used to obtain hydrodynamic performance in various waterways. Besides, the Savitsky formula is applied to a prismatic planing hull is presented. Chapter 3 Introduce the theory of planning hull, it explained the main characteristics and the form types of a planing hull. Also, it shows resistance types on a planing hull, and effect of confined water to wave pattern and viscous resistance. Chapter 4 covered the steps of CFD analysis; domain dimension selection, boundary condition siting, and grid generation strategy which is used finite volume method for prismatic planing hull analysis, when sailing through the shallow channel and open water. Chapter 5 discusses and validates numerical results obtained from a shallow water channel include wetted length, total resistance, hydrodynamic lift force, normal force, and wave pattern with experimental results. Also; compares total resistance and wave form with open water results. Chapter 6 includes conclusion for sailing effect in various waterways of planning vessel and recomm- endations for future works. 2. Literature Review This chapter reviews the techniques which using for planing hull analysis. Besides, the Savitsky formula is applied to a prismatic planing hull is presented. 2.1 Background Recently, the simulation of the hydrodynamic perfor- mance of planing hull sailing in shallow water has become a common practice in the yachts building community. Hence, it is more used for the high-speed boats which sailing near to shoreline compared with where yachts used to navigate before. The high demand for high-speed boats operating near shore requires good knowledge of their behavior in three regions of speed, (subcritical, critical, and supercritical). There are many methods to calculate hydrodynamic cha- racteristics of planing vessels such as analytical experimental Figure 3. analysis approaches used for planning hull[2] and numerical methods Figure 3 shows the most common of these methods including the numerical and experimental methods. The experimental methods require expensive facilities and measurement tools. These results increased cost and time compared with the numerical methods. Hence there has been an increase in the use of numerical methods for investigating the small boat resistance in different waterways such as shallow channels and open water. At the planing speed, the planing hull is supported by buoyancy force and lift force which put the hull in position, the wave-making resistance is the main component in the total resistance. The waves system includes the transverse wave and the divergent wave. The divergent wave angle starts for a 19.47 degree at subcritical speed (Kelvin wave pattern) and increases until 90 degrees at critical speeds after that decreases in supercritical speeds. The first theoretical formula to consider the calculation of maximum pressure around planing 2D sections was proposed by Kerman [3] , which was based on the conservation of momentum in the analysis. His work remained in use until equations for 3D planing surfaces by Savander and Scorpio [4] were introduced, which describes potential perturbation and vortex distribution around a planing plate. The finite difference method was used to solve the Kadomtsev-Petviashvili equation for a TSS model moving near critical speed [5] . By using the technique of matched asymptotic expansions along with nonlinear shallow-water wave theory, the problem is reduced to a Kadomtsev-Petviashvili equation in the far-field, matched with a near-field solution obtained by an improved slender-body theory, taking the local wave elevation and longitudinal disturbance velocity into account. The ship can be either fixed or free to squat. Besides wave pattern and wave resistance, the hydrodynamic lift force and http://dx.doi.org/10.36956/sms.v3i1.414
- 40. 36 Sustainable Marine Structures| Volume 03 | Issue 01 | January 2021 Distributed under creative commons license 4.0 trim moment are calculated by pressure integration in the fixed-hull case; running sinkage and trim, by the condition of hydrodynamic equilibrium in the free-hull case. Many experiments were conducted in an attempt to calculate the force and the moment on the flat bottom hull in shallow water at fixed trim by Christopher [6] . Furthermore, the force and moment on a constant deadrise angle prismatic hull by Reyling [7] were obtained experimentally. For series 62 hull form, residuary resistance was computed over a range of speeds from displacement speeds to planing speeds when the hull moving in shallow water and it was concluded that there is an increase in residual resistance at the subcritical speed range and a decrease at the supercritical speed range as compared to deep water. Besides, there was a resistance hump created at the maximum angle of trim and the highest value of sinkage. The 2D+t potential flow method was used to investigate the performance of planing hulls in calm water and was compared with 3D Reynolds Navier Stokes Equation (RANSE) method by Iafrati and Broglia [8] . A comparison between the two solutions is established to understand the role played by three-dimensional effects, neglected within the slender body assumption. The analysis is focused on the evaluation of the free surface shape and of the pressure field acting on the hull surface. It is shown that a good agreement is achieved in terms of the free surface profile, corrected is applied to the 2D+t solution to account for the rise up of the water in front of the hull. The comparison in terms of pressure distribution reveals important three- dimensional effects in the very fore part of the hull, in the region about the separation point, and at the transom. These methods-based designs were presented, starting from a non-stepped hull configuration, a multiple-step solution was developed and an optimization of the unwetted aft body area behind the steps was performed. The goal of the optimization is drag reduction and dynamic stability [9] . The validation of the 2D+t model for single stepped planing hull with the experimental data in terms of; resistance, dynamic trim, and wetted surface area was carried out by Bilandi [10] . The obtained hydrodynamic results have been compared against the experimental data and it has been observed that the presented mathematical model has reasonable accuracy, in particular, up to Froude number 2.0. Furthermore, this mathematical model can be a useful and fast tool for the stepped hull designers in the early design stage to compare the different hull configurations. It should also be noted that the mathematical model has been developed in such a way that it has the potential to model the sweep-back step and transverse the vertical motions of single-stepped planing hulls in future studies. The RANSE method was used to predict moment and force on a planing prismatic hull with a constant deadrise angle equal to 20 degrees by Brizzolara and Serra [11] . Results obtained, in terms of drag lift forces and longitudinal trimming moment, are compared with available experimental (model tests made at Hydronautics towing tank) and semi-empirical theories (Savitsky, Shuford, etc.) commonly used by a naval architect for the prediction of planing surface hydrodynamic performance. By the comparison of global force components and moments and the analysis of distributed parameters, such as pressure on the wetted hull, tangential stresses, spray root line, and wave elevations, some interesting conclusions can be drawn on the accuracy of CFD codes for the prediction of steady hydrodynamic performance of planing hulls. Safari calculated; total resistance, added resistance, and wave pattern numerically for model 4667-1 by using CFD software based on finite volume method to solve the RANS equations in different speed and depths including deep and shallow water conditions. Also, the wave pattern and flow field around the vessel are investigated. For validating the method, at first, the resistance results in deep water are compared with the experimental data and show good agreements. Simulations are performed in transient mode, using Volume of Fluid (VOF) and k -ε schemes to model the free surface turbulent flow. The results have shown that by decreasing the depth, the shallow water-resistance of a planing vessel will be increased [12] . Mancini used this analysis to get; total resistance coefficients, wetted surfaces, and dynamic trim for the parent hull model (C1 hull) from the Naples Systematic Series (NSS), Form hull characterized by a warped bottom [13] . Moreover, heave motion, pitch angle, free surface defo- rmation, and resistance of planing vessels were obtained through this analysis by Wang [14] in deep water. The modern transverse stepped planing hull was inve- stigated by CFD, which applies moving mesh techniques and large eddy simulation to find the total resistance, trim, and sinkage. These numerical results were validated with experimental results [15] . Moreover, Bakhtiari estimated; the numerical results of drag, pressure distribution, wetted surface, water spray, and wave generation by stepped planing hull [16] . Furthermore, the wake profile was compared by Savitsky and Morabito empirical formula. The morphing mesh method and k-ɛ model were used to simulate the fluid flow around the two- stepped hull moving freely to heave and pitch [17] . Also, this mesh technique was used to describe hydrodynamic characteristics around the tunneled planing hull and it’s http://dx.doi.org/10.36956/sms.v3i1.414
- 41. 37 Sustainable Marine Structures| Volume 03 | Issue 01 | January 2021 Distributed under creative commons license 4.0 compared with experimental results [18] . The Smoothed Particle Hydrodynamics (SPH) method investigated the pressure distribution on the seafloor in very shallow water and, the change in the angle of created divergent waves over a range of speeds [19] . 2.2 Savitsky’s Method This analysis for prismatic planing hull at fixed trim and fixed beam aims to find formulas for calculating the lift force, total resistance, wetted area, and center of pressure. There are two steps for this analysis, a simple case that imposes that all forces and moment affect in the center of gravity. Next, the general case includes lift, drag, and trust effect in a different point on the hull. The equations are depended on huge experimental results. The lift coefficients equation following as [20] (2.1) And Also (2.2) Figure 4. parameters for prismatic planing hull by Savitsky (1964) Figure 4 interpreted the dimensions and trim angle and deadrise angle β which are used to define the Savitsky geometry. From Savitsky formula notices that the lift force and drag is quale zero when the planing hull even keel. The drag force can be found by (2.3) The angle of trim has an important role to make the hull similar to a hydrofoil. It means that the hull has a drag force and lift. Moreover, the deadrise angle βis decreased lead to an increased lift force part. Finally, the longitudinal center of pressure calculates by (2.4) Where lp the distance from the stern to the center of pressure The total wetted bottom area of a planning surface is actually divided into two regions. One is aft of the spray- root line, commonly referred to as the pressure area and the other is forward of the spray-root line, referred to as spray area. The pressure area is load carrying area of the planning bottom. The forward spray area contributes to the total drag but is not considered to support any portion of load. An enlarged sketch of flow direction on a deadrise surface is shown in Figure 5. It is found that the flow in the pressure area is predominantly aft with some transverse floe along the chines. The flow along the spray- root line is primarily along the direction of the stagnation line. In the spray wetted area the direction of the fluid flow are such that the space angle between oncoming fluid particles and stagnation line is equal to the angel between the direction of the spray jet and the stagnation line; for example any line of motion in the spray area is nearly a reflection about the stagnation line of the incidence velocity direction. Since the pressure in the spray area is nearly atmospheric, then, by Bernoulli, the spray velocity can be assumed to be equal to the planing speed. Figure 5. flow direction along planning prism and extent of spray area [20] The total spray area, both sides, projected on a plane along the keel line is given by (2.5) The Savistly formula achieved good agreement with the experimental results but the CFD method work at a different type of modern hull shape and various waterways. 3. Planing Hull Theory This chapter displays the main characteristic of the planing hull and the type's form of planing vessel. It is defined the concept and types of resistances. Also, it shows the effect of restricted waterways on wave-making resistance and viscous resistance. 3.1 Planning Hull Characteristic The planing vessels sharing with Common features ha- ve a transom stern, bow carve, flat surfaces, hard chines, V shape at the transom, and maximum length does not exceed 30m. Figure 6 shows the main characteristics of http://dx.doi.org/10.36956/sms.v3i1.414
- 42. 38 Sustainable Marine Structures| Volume 03 | Issue 01 | January 2021 Distributed under creative commons license 4.0 the planing hull shape. Figure 6. main planing hull characteristics All the planing boat has one or more chine. It means the points crossing the bottom and hull-side. There are three types of chine; hard chine-like-angel, soft chine-like- carve, and reverse chine. A hard chine is meant to throw spray to the facets of the hull and to prevent water from flooding the hull where it will raise resistance. Chine with step has a considerable contribution of dynamic lift force. The soft chine gives smoother sailing for the hull compare with a hard chine boat. Whereas it is the maximum speed for hard chine boats higher than the soft chine boats [21] . Deadrise β is the angle a hull bottom makes with the horizontal plane viewed from ahead or astern. The right amount of deadrises gives a boat directional stability, a softer ride, and reduces wetted surface drag as the boat rises on a plane. Deadrise is said to be “constant” if it stays approximately the same from amidships to the transom. Deadrise is “variable” if it changes from a deep angle at amidships to a shallow angle at the transom. Figure 7. main dimension of planning hull The dimensions of the planning hull are considered the part of hull characteristics shown in Figure 7. LOA Overall length of the planing hull measure the distance from bow to stern LPP Length between perpendiculars, it changes when the hull starts moving TFP Draft forward measure the vertical distance between the base line and waterline in a bow TAP Draft aft measure the vertical distance between the base line and waterline in a stern The hull upright position, means don’t movies is TAP = TFP Trimming angel θ when the planning hull start go forward the bow rising and stern immersed. TAP ≠ TFP 3.2 Planing Hull Type The design of the planing hull boat improves with time to provide comfort, luxury, and increase the speed according to customer requirements and application of vessels. There are a lot of designs of planing to improve the performance of the hull. 3.2.1 Prismatic Planing Hull The Fridsma hull was produced in 1969 by the Davi- dson Laboratory. Figure 8 shows the form of a prismatic hull in which the deadrise angle β is fixed in all the stations and was one beam in length. The bow had identical planforms and elliptical keel profiles. When the β equals zero the hull is called the flat bottom. Figure 8. Prismatic Fridsma hull [22] 3.2.2 Warped Planing Hull Figure 9 shows the warped hull in which the deadrise angle changes along the length of the hull, for that demand the change of angle along with any station. This gradual change of angles from bow to stern transom gets better comfortable on board and decreases fuel consumption on a warped hull compared with a prismatic hull in the voyage. Figure 9. Warped hull of C945 models [23] Through time the planing hull design developed to decrease the total resistance and increase the speed. For that on the warped bottom hull, a spray rail was added to improve which improves the performance of the planing hull by M¨uller-Graf (1991). - Give more lift force due to the deflection of the spray. http://dx.doi.org/10.36956/sms.v3i1.414
- 43. 39 Sustainable Marine Structures| Volume 03 | Issue 01 | January 2021 Distributed under creative commons license 4.0 - Decreases the spray frictional resistance to reduce the spray wetted area on the hull. - Damping roll motion and more transverse stability. - Increases deck dryness. 3.2.3 Tunneled Planing Hull The tunnels divided the planning hull into three parts: the main hull and two side hulls. It is called a trimaran planning hull. Figure 10 shows the body plane of the tunneled planning hull. The tunnel gives an additional aerodynamic lift of the hull and less power required for racing. Figure 10. tunneled planing hull [18] 3.2.4 Stepped Planing Hull The modern designs of the planing hull have one or more transverse steps on the hull bottom. The transverse step separates the flow of water and ventilates the aft part of the hull. This causes a reduction of the wetted surface area, which leads to decreased friction resistance and improves the lift per unit area. Furthermore, it gives damping pitch motion and improves pitch control. Figure 11 shows the planing hull with one transverse step. Approximately, the stepped planing gives good performance at the high-speed range 1.5 ≤ Fn ≤ 1.75 and more drag at the displacement speeds compare with the same planing hull non-step. Figure 11. The C03 model buttock line and body plan [10] 3.2.5 Air Lubricated Planing Hull This type of boat is a lot ordinarily referred to as a planing hull. When to move at high speed the bow lifts out of the water and is carried by the passage of air under that, whereas the stern is in a displacement mode and is carried by the seawater. It is sailing similar to small boats and private watercraft. 3.3 Planing Hull Resistance The total resistance for bare planing hull in calm water RT excludes the added resistance by sea wave and wind. It is equal to the sum of the air resistance, viscous resistance, spray resistance, and wave-making resistance. RT=RA+RV+RS+RM(3.1) There is an effect of restricted waterways such as finite depth of water and baking effect. This waterway increases total resistance compared to the unrestricted waterway. 3.3.1 Air Resistance The air resistance is very little value in which the density of air is much smaller than the density of water. Spatially at low speed can be neglected. The air resistance can be calculated by RA=0.5ρacDAU2 (3.2) Where ρa the density of air, CDair frictional coefficient, A projected area on vessel above the waterline which facing the air, U speed of a vessel. 3.3.2 Viscous Resistance The viscous resistance RV concerns the main component of total resistance because its value depends on tangential force on the hull and vessel form. The viscous resistance includes frictional resistance, RF viscous pressure resistance RVP and Flow separation. The frictional resistance can be calculated by RF=0.5ρCFSU2 (3.3) CF The frictional coefficient for smooth hull founded by the International Towing Tank Conference (ITTC) 1957 for model ship. (3.4) Reynolds number is equal whereas kinematic viscosity. The factor (K) expresses all parameters related to the shape of the hull like the wake, the roughness of a surface, separation point, and eddies….etc. RV =(1+K)*RF (3.5) To explain the impact of fluid viscosity, boundary layer theory can be used. This means that viscosity matters only in a thin layer close to the surface of the hull. It is possible to use the two-dimensional boundary layer along a flat plate to define significant viscous flow characteristics. A flat plate can be approximate the moistened hull surface. If we look at the flow following the ship from a reference frame, the ship's ward speed appears as an incident flow on a stationary hull with velocity U, as shown in Figure 12. http://dx.doi.org/10.36956/sms.v3i1.414
- 44. 40 Sustainable Marine Structures| Volume 03 | Issue 01 | January 2021 Distributed under creative commons license 4.0 Figure 12. sachem of the boundary layer on a flat plate [24] 3.3.3 Spray Resistance Almost, the spray resistance starts to occur at Froude number 0.5 and rapidly increases with speed. Its effect of the spray resistance was divided to pressure resistance component and frictional resistance by M¨uller-Graf (1991). RS=RSP(Fn)+RSF(Rn,Wn)(3.6) Where RSP The function of Froude number, RSF the function of the Reynolds number and Weber number (3.7) VSR is the spray velocity, dSR is the spray thickness, and Ts is the surface tension at the water-air interface. A representative value of Ts 3.3.4 Wave Making Resistance The wave-making resistance RM is the second major component of the total resistance. It is the resistance of a wave, which is generated by the vessel when it moves in calm water. Three factors affect wave-making resistance: speed of the vessel, underwater hull form, and depth of water. The last factor that has a strong influence on Froude depth equal to one. The wave-making resistance cannot be easygoing for calculating it, like viscous resistance. Kelvin (1887) explain the waves system established when the pressure point moving in deep water. There are two types of waves. Figure 13 shows the divergent wave and the transverse wave in deep water. The crest divergent wave slopes 19.28ᵒ from the centerline. While the transverse wave perpendicular to the centerline 90ᵒ. However, it recently shows that this angle can be significantly smaller at large Froude numbers [25] . Figure 13. The Kelvin ship wave pattern in deep water. The included half-angle 19◦28_ of the waves is called the Kelvin angle and is affected by the water depth [26] 3.4 Restricted Waterways Effect The restricted waterways like the channels, lakes, ha- rbored, and so on. Have effects on the total resistance, because there is a restriction on the depth of water and width or both. show the depth Froude number Fh makes the essential role in divide the range of speed into three regions in shallow water (3.8) • Fh 1 the region of subcritical speed the wave pattern like in deep water. • Fh ≈ 1 the region of critical speed the divergent wave angle equals 90. • Fh 1 the region of supercritical the divergent wave angle equals 45 and disappear the transverse wave. 3.4.1 Effect on Wave-making Resistance In shallow water, when increases the Froude depth Fh the wave angle modified means that change the angle of divergent wave and transverse in waves system shown in Figure 14. That is a modification in the wave system as a result of wave retardation which means the wave speed in shallow water decrease than the deep water. All of that leads to a change of wave-making resistance in shallow water. Especially, at critical speed in a shallow water channel can show the solitary waves. When Froude depth was equal to 1, the speed of the ship was equal to the speed of the wave, which can be calculated by (3.9) Figure 14. wave pattern change in shallow water http://dx.doi.org/10.36956/sms.v3i1.414
- 45. 41 Sustainable Marine Structures| Volume 03 | Issue 01 | January 2021 Distributed under creative commons license 4.0 3.4.2 Effect in Viscous Resistance In shallow water, according to throttling the flow between the seabed and bottom hull, The fluid flow under the vessel increased speed and decreases pressure supported the hull, it’s called the backflow effect, the fluid speed rises leads to enhancement skin friction resistance, whereas decreases water pressure under the hull, its changes the position of the hull. Squat phenomena definition is combined between sinkage and trim when the hull moving in shallow water, sinkage resulting from pressure dropping under the hull. It causes a change of water plane area and moves center of gravity. If the center of gravity union with from center of buoyancy, the hull is upright. When the center of gravity move from the center of buoyancy, a trim angle is established between them. For all that, squat effect to: • Increases the total resistance, viscous resistance, and wave-making resistance when the vessel sailing forward. • The ship sailing slow-speed and has been losing part of maneuvering and steering. • There will be a drop in speed in shallow water as a result of increased resistance and reduced propulsion efficiency. • There is a greater tendency towards vibration as a result of propeller-induced vibration. A range of planing speed the hydrodynamic lift force work opposite direction to sinkage force. So the sinkage force reduces the buoyancy force which shares in put the planing hull in position. 4. Methodology of Analysis In this chapter, the methodology used in the CFD ana- lysis is described. The chart in Figure 15 shows the CFD results validation with experimental data. The work is started by defining the numerical domain dimensions. And then mesh elements are generated and boundary con- ditions for the numerical domain are set to carry out the analysis and obtain the results. Post-processing results are then assessed by comparing them to experimental data. If the results are in agreement with the experimental data the analysis is finished. Otherwise, modifications on mesh and setup are applied in the new analysis boundary condition and regenerating the mesh to make the new analysis until getting valid results. 4.1 Finite Volume Method In this research work, the finite volume RANS code ANSYS CFX was used to study the flow around a small planing hull craft in a shallow water channel to predict the hydrodynamic forces and wave patterns of the hull at subcritical, critical, and supercritical speeds. Figure 15. flow CFD analysis simulation The motion of a viscous fluid is governed by the Navier- Stokes equations, which are valid both for turbulent and laminar flow. For an incompressible, Newtonian fluid in three dimensions under the influence of an external gravitational field, [27] the Navier-Stokes read. (4.1) Here, the equations are formulated using tensor notation. The indices i and j in the Navier-Stokes equations run over the spatial coordinates x, y, and z. In these equations, Ui is the velocity in direction i, xi is the spatial coordinate in dimension i, t is time, ρ is the density, P is the pressure, ν is the kinematic viscosity and gi is the gravitational acceleration. The finite volume method (FVM), is a numerical method of discretizing a continuous partial differential equation (PDE), into a set of algebraic equations. The first step of discretization is to divide the computational domain into a finite number of volumes, forming what is called a mesh or a grid. Next, the PDE is integrated into each volume by using the divergence theorem, yielding an algebraic equation for each cell. In the centers of the cells, cell- averaged values of the flow variables are stored in so-called http://dx.doi.org/10.36956/sms.v3i1.414
- 46. 42 Sustainable Marine Structures| Volume 03 | Issue 01 | January 2021 Distributed under creative commons license 4.0 nodes. This implies that the spatial resolution of the solution is limited by the cell size since the flow variables do not vary inside a cell [28] . The FVM is conservative, meaning that the flux leaving a cell through one of its boundaries is equal to the flux entering the adjacent cell through the same boundary. This property makes it advantageous for problems in fluid dynamics [29] . A stationary transport equation involving diffusion and convection of a general flow variable, variable, φ, can be written as (4.2) Where Γ is the diffusivity and S is a source term that may depend on . By using the FVM, this equation can be written in discrete form as (4.3) Where (4.4) In these equations, where the summations run over all the nearest neighbors of each cell, φP is the value of the flow variable in the present cell and φnb are the values of the flow variable in the neighboring cells. SU and SP are the constant and flow variables depending on parts of the source term, respectively. Furthermore, aP is the discretization coefficient associated with the present cell, and are discretization coefficients describing the interaction with its neighboring cells. The discretization coefficients depend on the discretization schemes used to approximate the values of the flow variables on the cell boundaries, also known as cell faces. By using appropriate discretization schemes to determine the coefficients, a set of algebraic equations for the cell values is obtained. 4.2 Turbulence When a hull is moving through the water, the flow around the hull is turbulent. In this section, the governing equations of turbulent flows are presented and turbulence modelling is explained. 4.2.1 Turbulent Flow Turbulence has no physical definition, but it is char- acterized as a three-dimensional, irregular flow where turbulent kinetic energy is dissipated from the largest to the smallest turbulent scales. On the smallest turbulent scales, known as the Kolmogorov scales, the energy is dissipated into heat due to viscous forces. Since turbulence is a dissipative phenomenon, energy must be continuously supplied to maintain a turbulent flow. Analytical solutions to the Navier-Stokes equations only exist for a limited number of simple cases such as laminar flow between flat plates. For turbulent flows in engineering applications, analytical solutions do not exist and the Navier-Stokes equations must be treated numerically. If they are solved using direct numerical simulation (DNS), the velocity field of the flow is obtained. However, since turbulence occurs on a wide range of time and length scales, DNS requires very high temporal and spatial resolutions to capture all the details of the flow. Thus, DNS is very computationally expensive and time-consuming which limits the method to special applications such as academic research or simulation of simple flows. 4.2.2 Turbulence Modelling The most common way of treating turbulence is to use turbulence models in which the turbulent features of the flow are not resolved in time. By performing Reynolds decomposition, the instantaneous velocity and pressure can be decomposed as (4.5) Where and denote the time-averaged quantities while Ui and p are the fluctuating components of the velocities and the pressure. By inserting the Reynolds decomposition into the Navier-Stokes equations given in equation (4.1), the Reynolds averaged Navier-Stokes (RANS) equations are obtained. These are written as (4.6) It can be noted that the RANS equations are very similar to the Navier-Stokes equations except for the additional term including uiuj, referred to as the Reynolds stress tensor. If the Reynolds stress term is modelled, the RANS equations describe the time-averaged flow quantities which require substantially less computational resources in comparison to DNS. A common approach for modelling the Reynolds stress tensor of the RANS equations is to use the Boussinesq approximation. In this assumption, the Reynolds stress tensor is modelled as a diffusion term by introducing a turbulent viscosity, νt, according to (4.7) In this equation, δij is the Kronecker delta which assumes a value of 1 if i = j and 0 otherwise, and k is the turbulent kinetic energy defined as (4.8) By using a model to describe how the turbulent visc- osity depends on the flow, the RANS equations can be solved. The so-called two-equation turbulence models, such as the k-ε model and the k-ω model, use two additional http://dx.doi.org/10.36956/sms.v3i1.414
- 47. 43 Sustainable Marine Structures| Volume 03 | Issue 01 | January 2021 Distributed under creative commons license 4.0 transport equations to describe the turbulent viscosity. They are referred to as complete models since they allow the turbulent velocity and length scales to be described independently [30] . 4.2.3 Turbulence Model Standard k-ɛ The stander k-ɛ model reported by Launder et al., to obtain turbulent viscous using transport equation for turbulence kinetic energy k (4.9) And transport equation for dissipation rate ɛ (4.10) To obtain (4.11) Where , and are model constants and are variable coefficient for the model Pk is the production due to mean velocity shear Table 1 shows the model coefficients improved during the time by a group of scientists. Table 1. model coefficients improve with time model years C1 C2 Launder and Jones 1972 1.55 2 1.3 1 0.9 Sharma and Launder 1974 1.44 1.92 1.3 1 0.9 Spalding and Launder 1974 1.44 1.92 1.3 1 0.9 The turbulence k-ɛ has a reasonably accurate and com- putational cost per iteration compare with most anther turbulence models, resulting in that it used the engineering turbulence model for industrial applications. There is constrain for turbulence model at a wall, strong separation, curvature and large streamline for that must be using the wall function. 4.2.4 Turbulence Model k-ω The turbulence model k-ω reported by Wilcox [31] . It has a transportation equation for kinetic energy k and specified dissipation ω, this specific dissipation has a relation to dissipation ɛ according to (4.12) (4.13) And transport equation for (4.14) To obtain (4.15) Where β* , , σω, Cω1, and Cω2 are model constants. The merits of this turbulence model have good work near the wall and low turbulent regions [29] . Hence, it is valid too in the regions of turbulent Reynolds number locale near to the wall, lead to that the transport equations can be utilized within the entire stream space. The disadvantage of the k-ω model is that the results are sensitive to the choice of boundary conditions and initial conditions. 4.2.5 Turbulent Model SST Shear stress transport (SST) model described by Menter. This model is a hybrid turbulence model to get advantages k-ɛ and k-ω turbulent models. Figure 16 shows the Zonal work turbulence model k-ɛ k-ω around the flat plate, the first model applies in the wake and outward stream domain. Other models work around the wall it means sub and log layer. Figure 16. Zonal work turbulence model k-ɛ k-ω on a flat plate [32] According to a combination between k-ɛ and k-ω tur- bulence models, Transportation equation blended of them to obtain by (4.16) The SST k-ω model has shown good performance for many types of complex flows, such as inflows with adverse pressure gradients and separating flows. It has been recognized for its good overall performance [33] and it is the most commonly used turbulence model for simulations of ship hydrodynamics. A drawback needs a lot of time to solve problems combers with the k-ɛ turbulence model. 4.3 Geometry of Planing Hull The model employed in this study is the same as the one used in the Morabito experiment [34] whose model surface is shown in Figure 17. The model is a box shape whose dimensions are (length 914 mm, beam 183 mm, depth 102 mm ) with groove (9 mm high × 6 mm deep) http://dx.doi.org/10.36956/sms.v3i1.414
- 48. 44 Sustainable Marine Structures| Volume 03 | Issue 01 | January 2021 Distributed under creative commons license 4.0 around the model. It is located at 9 mm above the bottom. a) Experimental model (Morabito, 2013) b) Numerical model Figure 17. Planing hull model a) Experimental model (Morabito, 2013) b) Numerical model The test was adopted by moving the model through the channel at a constant sinkage and trim by aft is equal to 6 degrees. This angle raises the bow of the hull above the waterline leads to canceling the bow effect. The hull model is examined in calm water at a range of speed from 0.3 m/s to 3.7 m/s for water depths 0.5b, 0.75b, 3b, 8b. The objectives of the Morabito experiment are to measure the tangential force and normal force on the bottom of the hull separately using a dynamometer, also it’s calculated; the change in wetted chain length (LC), and transom ventilation (YK) at all range of speed. For that, the hull is divided into station and water line shown in a. 4.4 Planing Hull Computational Domain Due to the symmetry of the hull, only half of the com- putational domain is represented in the CFD simulations of this study with dimensions shown in Figure 18. The hull is implemented with a fixed trim of 6 degrees and fixed heave giving a transom draft of 0.05673 m, such as that in the experimental work. The study is carried out to simulate a shallow channel whose water depth is 0.1 L and width is 1.3 L. The reference point of the computational domain is at G= (0, 0, 0). Boundary conditions imposed on the numerical domain are shown in Table 2. The air-water flows through the shallow channel from inlet to outlet about the hull. These investigations cover a range of speeds from 0.3 m/s to 3.7 m/s. This range includes the three regions of the subcritical, critical, and supercritical speeds. Also, when the analysis for deep water, the high seabed equal to 2.46 L. This height ensures no effect for seabed on the hull resistance. Figure 18. Dimension and boundary conditions of the shallow domain Table 2. boundary conditions details Position type boundary condition Boat No-slip wall Inlet velocity inlet Outlet static pressure outlet Top free slip wall Side free slip wall Bottom free slip wall Symmetry - symmetry In this investigation, was selected the k-ɛ turbulence model depending on previous work [12] . At the critical speeds, when the ship velocity is equal to the velocity of the wave in a shallow channel, the solitary wave will be established every some time. The solitary waveform is a wave single crest that moves forward through the shallow channel. Hence, it’s required transient analysis at critical speed as shown in Table 3. The analysis at subcritical speeds and supercritical speeds are steady with time shown in Table 4. Table 3. analysis setting at critical speeds analysis type transient number of element 6000000 turbulent model k-ɛ Total time 25 sec time step 0.2 sec Table 4. analysis setting at subcritical and supercritical speeds analysis type Steady-state number of element 6000000 Residual e^-5 Max iteration 10000 turbulent model k-ɛ 4.4.1 Open Water Domain Dimension An open water analysis needs to make some changes http://dx.doi.org/10.36956/sms.v3i1.414
- 49. 45 Sustainable Marine Structures| Volume 03 | Issue 01 | January 2021 Distributed under creative commons license 4.0 in computational domain dimensions to cancel the seabed effect shown in Figure 19. When h/T ≥10 the effect of seabed to the hull is insignificant. In literature reviews of planing hull analysis in deep water, the depth of water is equal to three times the total length of the vessel. Moreover, the distance between the channel side and the hull equals two and a half time the hull length. This distance more than enough to remove the effect of channel-side into the hull. The small box around a hull as a domain uses to help capture physics phenomena at deep- water analysis. A small domain dimension is shown in Figure 19. Figure 19. Open water domain dimension 4.4.2 Distribution on the Hull In the current study, the wall bounding effects are very important and have a significant effect on the hull form drag at different speeds. Figure 20 shows the distribution y+ on the hull which a value around 30 to 300. This range of y+ gives the turbulence model a good chance to predict the flow around the hull form properly. Can be calculated. (4.17) u* Calculated by flowing formula (4.18) And (4.19) The distance from the surface of the hull to the first cell centroid considered it y, u* wall friction velocity, v kinematic viscosity, tw sheer stress, ρ dynamic viscosity, U speed of the vessel, Cf friction coefficient obtained by equation (4.3). 4.4.3 Free Water Surface To simulate a hull moving in water, models are needed to resolve the interface between the water and air. There are different two-phase models available that either tracks the surface directly or tracks the different phases and then reconstruct the interface. One example is the level-set a) hull side in shallow water b) hull bottom in shallow water a) hull side in open water b) hull bottom in open water Figure 20. distribution y+ on the hull method, where all molecules of one phase are marked and then tracked in the fluid flow. The most frequently used http://dx.doi.org/10.36956/sms.v3i1.414
- 50. 46 Sustainable Marine Structures| Volume 03 | Issue 01 | January 2021 Distributed under creative commons license 4.0 method to capture the free surface in ship hydrodynamics is the volume of fluid (VOF) method. In the VOF method, the different phases are tracked. The flow of the model is assumed to be an incom- pressible turbulent flow. Hence, the governing equations are the continuity and momentum equations given as foll- ows. (4.20) The Reynolds stress tensor represents the change of momentum cross the free-surface which occurs as a result of surface tension force, the color function describes the free-surface as the volume of fraction γ (4.21) Based on the volume of fluid (VOF) method, the air- water interface is described implicitly. The volume of fra- ction represents the percentage of water at each cell at the free surface to describe the interference between the two fluids. The magnitude of for each cell cut by the free surface is between 0 and 1 (0 1). While the volume fraction equals 1 for total water occupancy, it equals 0 for total air (4.22) (4.23) Where ρ and μ at any cell (denoted by ij) can be computed using by taking a simple volume average over the cell. Besides, (a) and (w) refer to air and water, respectively. 4.5 Mesh Generation Strategy Code ICEM CFD is used to generate an unstructured mesh grid required for the CFD code solver. For analysis in two waterways, it needs different strategies for building mesh in the computational domain. In shallow channel analysis is applied grid in one-domain is called single mesh, whereas a two-domain grid is used in open water analysis it is called overset mesh. 4.5.1 Single Mesh The number of mesh elements generated –in one domain- and shown in Figure 21 equals 6 million elements. The accuracy of results is dependent on the quality of the mesh grid which is affected by the element size, type, and algorithm. The number of mesh elements is increased over the planing hull surface and its vicinity to improve the accuracy of numerical predictions of resistance and wave patterns generated at different forward speeds. The mesh density function is applied at the free- surface region throughout the whole computational domain to better predict the generated wave patterns by the hull. A refined mesh is generated at the bottom of the computational domain to accurately predict the effect of the channel bottom on the hull resistance. a) Tetrahedral mesh all over the domain b)Mesh size around the hull Figure 21. Schematic illustration of generated 6 million mesh elements in one-domain. A) Tetrahedral mesh all over the domain b) Mesh size around the hull. 4.5.2 Overset Mesh The overset mesh creation is a bit more challenging than the one of the single mesh since it requires the creation of two computational domains: the background and the overlapping domain. The first one consists of a box of large dimensions, while the second one is smaller and contains the boat shown in Figure 19. The idea underlying the overset technique is that the increasing number of elements mesh around the hull without increase the total number of elements in the background domain. On the other hand, the drawback lays in the fact that there has to be communication between the two domains, through an interpolation across the domain boundaries of the smaller domain. To limit the effect of the interpolation to the minimum the meshes have to be built so that the size of the cells at the boundary of the overlapping domain is as close as possible to one of the background domains in that area. Once the domains are created, the same refinements presented for the single mesh are applied, with the only difference that some refinements will belong to the overset domain and some to the background. The free surface refinement will belong to both: an internal surface needs to be created in both domains. The wake refinement will be present only in the background domain, http://dx.doi.org/10.36956/sms.v3i1.414
- 51. 47 Sustainable Marine Structures| Volume 03 | Issue 01 | January 2021 Distributed under creative commons license 4.0 while the surface, curve, and undersea refinements will be added in the overset one. Another refinement is added in the background domain, to comply with the requirement of the same cell size in the area around the boundaries of the overset domain. This is done by inserting in the background domain a box refinement around the overset domain with a target cell size of 0.04 m. Figure 22 shows tetrahedral mesh around the two domains and presium layer around the hull. a) Tetrahedral mesh all over the domain b) Presium layer around the hull Figure 22. overset mesh generation 1.6 million mesh element 4.6 Mesh Dependence Study The one-domain mesh generation gives a tremendous number of elements when the change element size. To improve the results a grid dependence study has been made for three generations of mesh 3, 6, and 9 million elements. The grid which has 6 million elements give the best results for all range of speeds and suitable time of analysis. Table 5 shows the error of numerical results for three mesh element comber with experimental resistance and lift force. The minimum error can be founded for the last speed in the range of critical speed 1.2 m/s achieved by 6 million elements. 4.7 Solution When achieved convergence criteria for total resistance and lift force, the analysis was completed to solve the problem. Figure 30 explains the value of total resistance, lift force, and trim moment in the critical speeds range. 4.8 Post-processing After the analysis was finished, the post-processing tool was utilized to describe the contour in the waterline, dynamic pressure contour on the hull, and the relation between time and resistance or force or trim moment. 5. Results and Discussion This study predicted total resistance, generated wave pattern and, lift force of a planing hull model moving in a shallow channel over three regions of speed, (subcritical, critical, and supercritical). Numerical results were vali- dated by comparison with experimental data available in the literature [14] . 5.1 Comparison of Numerical and Experimental Results This part of the thesis compares to available exper- imental results; wetted length, dynamic normal force over displacement, and lift force coefficient with the numerical results. Notice that, at the critical speed range the value of results fluctuates. For that, the mean value was obtained numerically and compared with the mean value experimentally. The mean value of results can be calculated as (5.1) Where Xm = mean result at a critical speed n = number of results Xk = results at a critical speed. Furthermore, the percentage of error calculated from this equation: (5.2) Where Vex = experimental value Vnu = numerical value http://dx.doi.org/10.36956/sms.v3i1.414 Table 5. mesh dependence study for speed 1.2 m/s Force on hull (N) Experimental (N) 3 Million Elements Error (%) 6 Million Elements Error (%) 9 Million Elements Error (%) Total Resistance 4.80 6.64 38 6.08 27 6.33 32 Lift Force 41.75 58.98 41 56.03 34 58.00 39
- 52. 48 Sustainable Marine Structures| Volume 03 | Issue 01 | January 2021 Distributed under creative commons license 4.0 5.1.1 Comparison SST k-ɛ Turbulence Model The results of the total resistance are calculated from two turbulences mode SST k-ɛ. The SST model spends more time to arrive at the result comber with the k-ɛ model. Because the SST model solves two transport equations. Table 6 shows comparison between two errors of total resistance when using SST k-ɛ model. In the SST turbulence model, the error of resistance is near to 1% in middle high speeds. Whereas in last high speed the error of the k-ɛ model lower than an error of the SST model. After that, the error of an SST and k-ɛ model are Convergent in all ranges of speed. In this investigation was selected the k-ɛ turbulence model for all range of speeds. Table 6. comparison total resistance using turbulent mode SST and k-ɛ Velocity (m/s) RT,Exp (N) RT (k-ɛ) (N) RT (SST) (N) (k-ɛ) Error 100% (SST) Error 100% 0.3 0.309 0.307 0.307 0.555 0.606 0.5 0.670 0.789 0.798 17.792 19.041 0.6 1.215 1.123 1.202 7.506 1.050 0.8 2.513 2.755 2.755 9.642 9.642 0.9 3.736 3.582 3.473 4.134 7.058 1 4.416 4.294 4.227 2.759 4.284 1.1 4.946 5.351 5.298 8.189 7.119 1.2 4.801 6.080 6.011 26.628 25.201 1.3 4.787 4.905 4.793 2.458 0.115 1.4 4.856 5.049 4.904 3.983 0.993 1.5 5.007 5.214 5.051 4.137 0.882 1.8 5.814 5.792 5.588 0.374 3.879 2.4 7.737 7.255 6.958 6.225 10.066 3 9.771 9.029 8.660 7.596 11.371 3.7 12.379 11.645 11.172 5.928 9.753 5.1.2 Wetted Length Lc The wetted length expresses the chine length under the waterline Figure 23 shows the comparison between the wetted length to beam ratio of the model versus Froude depth numerically and experimentally. When the Froude depth near 1, the wetted length increases, as a result of solitary wave formation at the critical speeds range. However, all the ranges of numerical results achieved an error of around 4.8 % compared with experimental results. The maximum error at Fh= 1.27 is equal to 31.3 %. Approximately, at the supercritical speeds range, the wetted length is steady at 3.45 while, at subcritical speeds, it’s slightly fluctuated around 3.25. In general, the numerical results of Lc/ b showed an excellent agreement with the results of the experiment. Figure 23. Comparison between experimental and CFD results of wetted length/beam ratio at different Froude depth 5.1.3 Normal Force (N) The normal force means the hydrodynamic force acts perpendicularly to the hull bottom. Figure 24 shows the dy- namic normal force to static buoyancy force ratio, versus the Froude beam (CV) numerically and experimentally. The experimental normal force ratio slightly decreases below zero at the low-speed range before full ventilation at the transom occurs, which means the dynamic force applies suction on the hull toward the channel bed (squat force). The numerical normal force slightly decreases at partial ventilation. Then it increases rapidly until the dynamic force equal to displacement force at full ventilation at transom as a result of hydrodynamic lift. At this range the largest deviation between experimental and numerical results occurs. After that, the curve increases sharply without a considerable deviation between numerical and experimental results. The numerical results are lined with experimental results. Figure 24. dynamic normal force/static normal force ratio N/Δ versus Cv numerically and experimentally 5.1.4 Total Resistance RT A comparison between the numerical total resistance and experimental total resistance is shown in Figure 25. The two curves increase sharply over the critical Froude http://dx.doi.org/10.36956/sms.v3i1.414
- 53. 49 Sustainable Marine Structures| Volume 03 | Issue 01 | January 2021 Distributed under creative commons license 4.0 number’s range (Fh=0.84 to Fh=1.27). After that, there is a slight drop in values and then they increase gradually over the supercritical Froude number’s range (Fh=1.37 to Fh=3.9). Very good agreement between the two curves is observed over –almost- the whole range of Froude numbers albeit, the error increases at Froude number close to the peak. The total average error between the numerical and experimental total resistance is no more than 8%. The maximum error is observed at the maximum critical Froude-depth number of 1.27 and is equal to 26%. Figure 25. Experimental and numerical total resistance 5.1.5 Hydrodynamic Lift Force LF A comparison between the numerical and experimental total lift force is shown in Figure 26. In general, the lift force decreases slightly over the supercritical range (from Fh=0.32 to Fh=0.63). There is a numerical over prediction of the lift force in this range. However, the lift force increases rapidly over the critical speed range (from Fh=0.84 to Fh=1.27). Subsequently, the value of the lift force rises gradually over the supercritical range. The total average error equals 7%, while the maximum error is 34% at the maximum critical Froude-depth number. For the whole range of speeds, very good agreement is observed between the numerical and experimental lift force except the maximum critical speed of 1.2 m/s. Figure 26. Experimental and numerical Lift force 5.1.6 Wave Pattern The numerical and experimental wave pattern is similar at speed 0.3 m/s as shown in Figure 27. The free surface deformation at the displacement speed of 0.3m/ s is not significant. In the low-speed region, there is no high deformation at the hull side, the wetted chine experimentally and numerically equal to 558.8 mm and 620.9 mm sequentially. Also, there is partial ventilation at transom equals 2.12 mm experimentally and 3.23 mm numerically. a) Experimental Wave pattern [34] b) Numerical wave pattern Figure 27. Wave pattern comparison at speed of 0.3 m/s Figure 28 shows a similarity in the generated wave pattern numerically and experimentally at a speed of 1.8 m/s. There is a high deformation on the free surface at the planing speed of 1.8 m/s. While the waves about the hull side increase in height leading to an increase in wetted chine equal to 635.42 mm numerically and 609.6 mm experimentally, further the free surface drops at the transom. For the numerical and experimental generated wave pattern, high deformation occurs on the free surface and full ventilation at transom equal to 56.73 mm experimentally and 59.03 mm numerically. a) Experimental wave pattern [34] http://dx.doi.org/10.36956/sms.v3i1.414
- 54. 50 Sustainable Marine Structures| Volume 03 | Issue 01 | January 2021 Distributed under creative commons license 4.0 b) Numerical wave pattern Figure 28. Wave pattern comparison at speed 1.8 m/s 5.2 Solitary Wave Formation and Effects In this section, the complex hydrodynamic phenomena of solitary wave or soliton formation will be discussed. The solitary wave requires a specific situation to occur such as a shallow channel waterway. When a hull is moving at critical speed in a shallow channel, the solitary wave will be observed. Table 7 shows the solitary waves establishment positions, and amplitudes for critical speeds which are 0.8, 0.9, 1, 1.1, 1.2 m/s. The amplitude of the solitary wave increases with the increase in wave speed. At speed 1.2 m/s, the generated solitary wave is at amidships which has the highest amplitude of 0.05960 m. The solitary wave shifts forward till positioned at the front of the hull, which leads to fluid flow about the hull to be more complex. Table 7. properties the solitary wave at a range of critical speed Critical speed m/s location of the wave formation numerically Maximum wave amplitude (m) location of the wave formation experimentally 0.60 No wave - No wave 0.80 3.3m ahead of model 0.0133 3 m ahead of model 0.90 1.2m ahead of model 0.0263 1 m ahead of model 1.00 0.3m ahead of model 0.035 at Bow 1.10 at Bow 0.0414 at Amidships 1.20 at Amidships 0.0596 supercritical swept 10-deg The solitary wave establishes itself at different loca- tions along the hull within the range of critical speeds and moves forward on the hull with time. Figure 29 shows the solitary wave formation steps. Firstly, the divergent waves hit the channel side at t = 2.4 seconds and increase the pressure on the channel sidewall. Secondly, the waves are reflected from the channel side and encounter other divergent waves generated from the hull after 4 seconds. Thirdly, the solitary wave becomes Perpendicular to the hull at t = 10 seconds. Afterward, the wave shifts forward at t = 14.2 seconds until the maximum amplitude formulates at a position of 0.3m after the hull. The next pulse of the wave is produced at t = 18 seconds. As the hull moves in a shallow channel, it produces a pulse wave repeated every 23 seconds. t=2.4sec t=4sec t=10sec t=14.2sec t=18sec 1 2 3 4 5 http://dx.doi.org/10.36956/sms.v3i1.414
- 55. 51 Sustainable Marine Structures| Volume 03 | Issue 01 | January 2021 Distributed under creative commons license 4.0 t=23sec Figure 29. Solitary wave formulation steps at speed 1m/s Figure 30 represents the change in a trim moment, lift force, and total resistance on a half planing hull at the critical speed versus time. The maximum trim moment and maximum lift force occur on the hull at the same time. The effect of a solitary wave on the moment and lift force curves is like a sinusoidal wave. The instantaneous values of the trim moment and lift force relate to the location of the solitary waveform and speed. The direction of a total resistance an opposite to inlet flow. Speed 0.8 m/s Speed 0.9 m/s 6 speed 1 m / s Speed 1.1m/s Speed 1.2 m/s Figure 30. Solitary wave effects on a trim moment, lift force, and total resistance http://dx.doi.org/10.36956/sms.v3i1.414
- 56. 52 Sustainable Marine Structures| Volume 03 | Issue 01 | January 2021 Distributed under creative commons license 4.0 5.3 Comparison between Hydrodynamic Perfo- rmance in Shallow Water Channel and Open Water In this section of a results comber, the dynamic pressures distribution on the hull, wave patter at critical speed, and total resistance change between shallow water channel and open water. 5.3.1 Dynamic Pressure Distribution on the Hull Figure 31 shows the difference between the shallow channel and deep water hydrodynamic pressure around the hull at a critical speed. Figure 31 (b) and (d) show an increased wetted surface area on the hull side and hull bottom in a shallow channel at critical speed compared with the wetted surface area in open water that is shown in Figure 31 (a) and (c). The maximum hydrodynamic pressure in Figure 31 (b) and (d) at hull piercing on the water. It’s higher than that on the hull in (a) and (c). The pressure distributions around the hull at critical speed are unstable with time as a result of solitary wave formation. 5.3.2 Wave Pattern at Critical Speed Figure 32 (a) and (b) show the wave elevation com- parison between open water and shallow water channel at speed (1 m/s). The maximum wave height at the shallow water channel equals 0.069 m. On the other hand, the maximum wave height at deep water equals 0.038 m. The elevation of the wave in the shallow channel increases by about 81% from deep water. In shallow channels (Figure 32 (b)), the wave elevation increases, and the solitary wave occurs at a critical speed surface which leads to an increase in the wave-making resistance compared with open water. 5.3.3 Total Resistance (RT) Figure 33 explains the total resistance in deep water for three regions of speed. When the Froude number is in the range of 0.5 this called displacement mode, and the total resistance increases with speed. For semi-displacement speed, the hump of resistance occurs at 0.5 Fr 0.85 as a result of superposition in the wave system. After that, the total resistance increases with speed at the planing range Fr0.85. This general figure for the total resistance of the planing hull in deep water is similar to the deep water carve in Figure 34, which shows the total resistance in shallow water channels compared with the total resistance in deep water. The total resistance in deep water at low speeds is not exactly similar to the total resistance in the shallow channel. Firstly, in the deep water, the chart increases gradually until the appearance of the hump which increases resistance as a result of a superposition between two crests or two troughs in the wave system. http://dx.doi.org/10.36956/sms.v3i1.414 a) Pressure on hull side at deep water (b) Pressure on hull side at shallow water (C) Pressure on hull Bottom at deep water (d) Pressure on hull Bottom at shallow water Figure 31. Hydrodynamic pressure around the hull
- 57. 53 Sustainable Marine Structures| Volume 03 | Issue 01 | January 2021 Distributed under creative commons license 4.0 Also, there is a hollow that causes the total resistance to decrease because the crest cancels the trough in the wave system. Secondly, the total resistance in shallow water is rising rapidly in the critical speed period (0.8---1.2 m/ s). The maximum difference between total resistance in shallow water and total resistance in deep water is equal to 43% at speed 0.9 m/s. The total resistance in the supercritical speed range increases dramatically with speed increase. Lastly, the total resistance in the shallow channel is much higher than the total resistance in open water. Figure 33. Total resistance of planing hull in deep water [35] Figure 34. Comparison between the total resistance in shallow water channel and the total resistance in deep water 6. Conclusions and Recommendations In this chapter, the conclusion, and recommendation of the results from the numerical simulations of the prismatic planning hull when moving in various waterways, is presented. 6.1 Conclusions In this thesis, the RANS equations are solved by ANSYS- CFX code to simulate a small high-speed hull form moving in a shallow channel and open water. The total resistance and wave pattern of the planing hull model at three regions of speed (subcritical, critical, and supercritical) moving in the shallow channel have been numerically simulated. • In the shallow water channel, the total average error equals 7% for numerical lift force, 8% for numerical total resistance compared with available experimental results. The numerical analysis well captured the wave pattern. The numerical results give good agreement over the whole range of speeds with the experimental results except at the maximum critical speed of 1.2 m/s which resulted in an error equal to 34% for lift force and 26% for total resistance. • In the current study, the steps of the solitary wave formulation have been described at critical speeds. The amplitudes of the solitary waves were determined at the critical speeds of 0.8, 0.9, 1, 1.1, and 1.2 m/s where the amplitudes were found equal to 0.0133, 0.0263, 0.035, 0.0414, and 0.0596 m respectively. • The amplitude of the solitary wave increases when- ever there is an increase in the critical speeds. Also, this investigation defined the locations of the solitary wave formulation. Solitary wave formulates in front of the hull at the lower range of critical speed. However, at the higher range of critical speed, it formulates on the hull. • The solitary wave formation increases the wetted surface area and the free surface deformation. Also, causes fluctuation in the trim moment, and lift force on the planing hull depends on the location and amplitude of the solitary wave. The total resistance on the hull in the http://dx.doi.org/10.36956/sms.v3i1.414 (a) Wave elevation in a deep water (b) Wave elevation in a shallow water Figure 32. wave systems comparison between a shallow channel and open water at speed 1m/s
- 58. 54 Sustainable Marine Structures| Volume 03 | Issue 01 | January 2021 Distributed under creative commons license 4.0 shallow channel is higher than the total resistance in open water. The maximum difference is 43% which takes place at a critical speed of 0.9 m/s. In conclusion, the worst effect on the planing hull in shallow channels occurs at the critical speed range, where solitary wave formulates. So boat drivers must avoid sailing at a critical speed range. 6.2 Recommendations The future working which can be recommended: • Further study for critical speed ranges and behavior of hull motion especially the last speed in the range. • Using various mesh strategies and different software for this investigation to do more verification of results. • Additional research concerns the effect of channel dimensions and vessel shape on the formation of a solitary wave at a critical speed. • Research on how to apply solitary wave energy in useful engineering applications. References [1] G. For et al., “GUIDELINES FOR WING-IN- GROUND CRAFT,” Int. Marit. Orgnization, vol. 44, 2018. [2] R. Yousefi, R. Shafaghat, and M. Shakeri, “Hyd- rodynamic analysis techniques for high-speed planing hulls,” Appl. Ocean Res., vol. 42, pp. 105–113, Aug. 2013. DOI: 10.1016/j.apor.2013.05.004. [3] V. Kerman, “The Impact on Seaplane Floats During Landing,” Washington, No 321, 1929. [4] T. B.R, Savander, S.M, Scorpio, R.K, “Steady Hydrodynamic Analysis of Planing Surfaces,” J. Sh. Res., vol. 46, pp. 248–279, 2002. [5] S. D. S. Xue-Nong Chen, “A Slender Ship Moving at a Near-Critical Speed in a Shallow Channel,” J. Fluid Mech., vol. 291, no. May 1995, pp. 263–285, 1995. DOI: 10.1017/S0022112095002692. [6] K. W. Christopher, “Effect of Shallow Water on the Hydrodynamic Characteristics of a Flat-Bottom Planing Surface.” NACA, Washington, 1956, [Online]. Available: http://www.dtic.mil/cgi-bin/ GetTRDoc?Location=U2amp;doc=GetTRDoc. pdfamp;AD=ADA377307. [7] C. J. Reyling, “An experimental study of planing surfaces operating in shallow water,” new jersey, 1976. [8] A. Iafrati and R. Broglia, “Comparison between 2D+t potential flow models and 3d rans for planing hull hydrodynamics,” Ital. Sh. Model Basin, no. December 2016, pp. 6–9, 1955. [9] F. Di Caterino, R. Niazmand Bilandi, S. Mancini, A. Dashtimanesh, and M. DE CARLINI, “A num- erical way for a stepped planing hull design and optimization,” NAV Int. Conf. Sh. Shipp. Res., no. 221499, pp. 220–229, 2018. DOI: 10.3233/978-1-61499-870-9-220. [10] R. N. Bilandi, S. Mancini, L. Vitiello, S. Miranda, and M. De Carlini, “A validation of symmetric 2D + T model based on single-stepped planing hull towing tank tests,” J. Mar. Sci. Eng., vol. 6, no. 4, 2018. DOI: 10.3390/jmse6040136. [11] S. Brizzolara and F. Serra, “Accuracy of CFD codes in the prediction of planing surfaces hydrodyamic characteristics,” 2nd Int. Conf. Mar. Res. Transp., pp. 147–158, 2007, [Online]. Available: http://www. icmrt07.unina.it/Proceedings/Papers/B/14.pdf. [12] A. S. Amir H. Nikseresht, “Numerical investigation of shallow water resistance of a planing vessel,” Int. J. Civ. Struct. Eng., vol. 3, no. 1, pp. 164–168, 2016. DOI: 10.15224/978-1-63248-083-5-62. [13] S. Mancini, F. De Luca, and A. Ramolini, “Towards CFD guidelines for planing hull simulations based on the Naples Systematic Series,” 7th Int. Conf. Comput. Methods Mar. Eng. Mar. 2017, vol. 2017- May, no. May, pp. 0–15, 2017. [14] Y. Wang, “Numerical Prediction of Resistance of Planning Vessel with RANS Method,” Int. Conf. Comput. Sci. Autom. Eng., no. Iccsae 2015, pp. 287– 293, 2016. DOI: 10.2991/iccsae-15.2016.55. [15] A. De Marco, S. Mancini, S. Miranda, R. Scogna- miglio, and L. Vitiello, “Experimental and numerical hydrodynamic analysis of a stepped planing hull,” Appl. Ocean Res., vol. 64, pp. 135–154, 2017. DOI: 10.1016/j.apor.2017.02.004. [16] M. Bakhtiari, S. Veysi, and H. Ghassemi, “Numerical Modeling of the Stepped Planing Hull in Calm Water,” Int. J. Eng., vol. 29, no. 2, 2016. DOI: 10.5829/idosi.ije.2016.29.02b.13. [17] A. Dashtimanesh, A. Esfandiari, and S. Mancini, “Performance prediction of two-stepped planing hulls using morphing mesh approach,” J. Sh. Prod. Des., vol. 34, no. 3, pp. 236–248, 2018. DOI: 10.5957/JSPD.160046. [18] F. Roshan, A. Dashtimanesh, and R. N. Bilandi, “Hydrodynamic charactrisrics of tunneled planing hull in calm water,” Brodogradnja, vol. 71, no. 1, pp. 19–38, 2020. [19] A. Tafuni, I. Sahin, and M. Hyman, “Numerical investigation of wave elevation and bottom pressure generated by a planing hull in finite-depth water,” Appl. Ocean Res., vol. 58, pp. 281–291, 2016. DOI: 10.1016/j.apor.2016.04.002. [20] D. Savitsky, “Hydrodynamic Design of Planing Hulls,” Mar. Technol., vol. Vol. 1, no. No. 1, p. PP. 71-95, 1964. [21] K. Potgieter, “Planing Hulls.” South Africa, pp. 1–5, http://dx.doi.org/10.36956/sms.v3i1.414
- 59. 55 Sustainable Marine Structures| Volume 03 | Issue 01 | January 2021 Distributed under creative commons license 4.0 2018. [22] G. Fridsma, “A Systematic Study of the Rough- Water Performance of Planing Boats,” 1969. [Online]. Available: http://oai.dtic.mil/oai/oai?verb=getRecord metadataPrefix=htmlidentifier=AD0708694. [23] F. De Luca and C. Pensa, “The Naples warped hard chine hulls systematic series,” Ocean Eng., vol. 139, no. March, pp. 205–236, 2017. DOI: 10.1016/j.oceaneng.2017.04.038. [24] F. M. White, Fluid mechanics, 7th ed. Boston: (Mc- graw-Hill series, 2003. [25] M. Rabaud and F. Moisy, “Ship wakes: Kelvin or mach angle?,” Phys. Rev. Lett., vol. 110, no. 21, 2013. DOI: 10.1103/PhysRevLett.110.214503. [26] J. N. Newman, Marine Hydrodynamics, 40th ed. Cambridge: MIT press, 1977. [27] D. Frisk and L. Tegehall, “Prediction of High-Speed Planing Hull Resistance and Running Attitude. A Numerical Study Using Computational Fluid Dynamics,” CHALMERS UNIVERSITY OF TECHNOLOGY, Gothenburg, Sweden, 2015. [28] W. M. H K Versteeg, An introduction to comput- ational fluid mechanics., 2nd ed., vol. M. Perason Prentic Hall, 2007. [29] B. Andersson, R. Andersson, L. Håkansson, M. Mortensen, R. Sudiyo, and B. van Wachem, Compu- tational Fluid Dynamics for Engineers. Cambridge University Press, 2011. [30] L. Davidson, An Introduction to Turbulence Models. Gothenburg: Chalmers university of technology, 2018. [31] D. C. Wilcox, Turbulence Modeling for CFD, Third edit. La Canada, California: DCW Industries, Inc, 2006. [32] I. F. Training, “Modeling Turbulent Flows [REVISIT],” 2006, [Online]. Available: http://www.southampton. ac.uk/~nwb/lectures/GoodPracticeCFD/Articles/ Turbulence_Notes_Fluent-v6.3.06.pdf. [33] T. Bardina, J., Huang, P., Coakley, “Turbulence Modeling Validation , Testing , and Development,” California, 1997. [34] M. G. Morabito, “Planing in Shallow Water at Critic- al Speed,” J. Sh. Res., vol. 57, no. 2, pp. 98–111, 2013, doi: 10.5957/josr.57.2.120031. [35] Marshall R, All about Powerboats: Understanding design and performance, McGraw-Hill Professional, 2002. Nomenclature Latin Symbols L Length of planing hull (m) B Beam (m) h Depth of water (m) U Speed of the vessel (m/s). g Gravitational acceleration (m/s2 ) RT Total resistance (N) RV Viscous resistance (N) RA Air resistance (N) RS Spray rail resistance (N) RM Wave making resistance (N) LF Lift force (N) Fn Froude number Cv Beam Froude number, Fh Depth Froude number P Pressure (N/m2 ) tw Sheer stress (N/m2 ) Cf Frictional resistance V Speed of a wave (m/s) Lc Chine wetted length (m) LK Length of the keel (m) N Dynamic normal force (N) CLβ Lift coefficient CL0 Lift coefficients atβ = 0◦ FLβ Lift force (N) FL0 Lift force at β= 0◦ Greek Symbols α Kliven angle (degree) β Deadrise angle (degree) ρ The density of the fluid (kg/m3 ) τ Trim angle of planing hull (radian). v Kinematic viscosity (m2 /s) λw Mean wetted length-to-beam ratio, λw = 0.5(LC+LK/B) , Taken rang λw ≤ 4. τdeg The trim angle of the planing hull (degree) take rang 2◦≤ τdeg≤ 15◦ Abbreviation FDM Finite Different Method FVM Finite Volume Method FEM Finite Element Method BEM Boundary Element Method 2D Tow dimension 3D Three dimensions VOF Volume of Fluid CFD Computational Fluid Dynamic RANSE Reynolds Navier Stokes Equation ACV Air Cushion Vehicles SES Surface-Effect Ships SWATH Small water-plane-area twin-hull ITTC International Towing Tank Conference PDE Partial Differential Equation DNS Direct Numerical Simulation SPH Smoothed Particle Hydrodynamics http://dx.doi.org/10.36956/sms.v3i1.414